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Open science. Back to basics

Are we really sharing all steps in our research? And are we making clear which steps we cannot share and why? There are always hidden steps that influence our research design and reporting.

Ask yourself this question: If someone else were to pick up one of your studies, most likely a published paper reporting the results of one of your studies,  would they be able to understand and reproduce everything that you did?

Are there things that can’t be exactly reproduced, like privacy-protected data, or subjective, interpretive, or ethnographic experiences in your study? If so, have you pointed this out for the reader?

These sound like trivial questions.

Anyone outside of science would think these things must be true. They would think that of course a scientist, a researcher, would report everything they did in their research. Isn’t that their job? How else should science look? What else are scientists doing other than doing research and reporting that research?!

Maybe your answer to this question is, ‘yes, I have reported everything I did’. You truly believe that people could follow everything you’ve done, but is this really the case?

I work in the area of statistical analysis, mostly, and in this area, there is code required to run statistical analyses, and that code is the basis of my research design, assuming I didn’t collect the data myself.

So, I have secondary data, I already have data, and then I analyze it, and my code is the basis for that analysis. Well, it turns out there’s a lot of things in my code that others would not be aware of without the code. So, this makes code sharing absolutely essential. And we’ve come a long way in this area.

Economics and political science especially, in general, have strong code-sharing norms for people who do statistical analysis. Sociology is a little further behind. Probably one of the main reasons for this is that journals have not adopted strong policies of code sharing in sociology, unlike in political science and economics. Either way, as a researcher, I should be responsible for making my results reproducible, but let’s think of an even more hidden case.

In your statistical analysis, did you do things that are not in the code? Now, if any researcher were to answer ‘no’ to this question, I would not believe them, including myself. There are always things that we do, that we then take out of the code because they seem redundant or unnecessary or, in some cases, don’t make our study look as good as we want it to look. One of those things is to run models that we don’t report.

Now, that would be fine if we just happened to run a model on accident or had some other glitch or mistake. But a lot of the models we run are intentional, and the reason we don’t report them is that they produce results that we don’t want. They produce results that don’t support what we think is true or what we want to appear to be true because it’s sexy, because it’s something that would be publishable.

But the question to ask is then: have any of those extra models that I’ve run or you’ve run been used to inform decisions that we make in terms of what models to run next, how to recode any variables in those models, and what to report on, in the paper and in the code? And if the answer is yes, then that’s part of the research design, and should be reported.

Now, this is not the norm at all. I don’t know any discipline where this is the norm, and I don’t know people who are trying to make this the norm. This is a hidden area of the open science movement for the most part, although we have research now that overwhelmingly suggests that the findings that we’re reading about, that people are reporting on, are probably selected (thanks to p- and z-curve analysis). They’re probably a subset of findings that are not just a random subset of the models that researchers intentionally ran, but a selected subset in the sense that they all point in a certain direction or they all have a larger size or there’s something about them that makes them specially selective – and this selection process reduces the reliability of science.

Therefore, when I say back to basics, I actually mean the basics of research, not the basics of the open science movement. I mean reporting everything in the research process that influences the results. That would truly be open science.

Image credit: Nate Breznau’s own photo

Trump is my president. Let’s talk

In a democracy, the democratically elected president is the president. Citizens who reject this, promote an agenda against democracy itself.

I am a U.S. citizen and therefore Trump is currently as of October 2025, my president. This is the reality whether I like it or not. Trump is the democratically elected president of the United States. He won an election unanimously that was relatively uncontested by observers, and for the first time ever he is a Republican candidate who would still have won even if all non-voters had voted.

A common slogan in recent anti-Trump protests is “Not my president“. The slogan “Not my president” is a symbolic rejection of democracy. The (mostly Democrat voting) protestors reject the results of a democratic process. The implications of this protest message, are that the protestors would suspend democracy to have a different outcome if they could. The reason that it is ‘OK’ in this case to suspend democracy would be because Trump is “wrong”, “evil” or  “unhinged“. These are the same protestors who bewail the decline of democracy BTW.

The mass January 6th protest against the 2020 election results, and insurrection by those who tried to or forcibly enter the Capitol, is basically the same. It was based on rejection by (the mostly Republican voting) protestors of the Democratic election result of Biden as president. It, like the “Not my president” slogan, was a rejection of the Democratic system itself. Because one group did not like the outcome. The implications are that we should throw out democracy to have a different outcome.

The two sides who are regularly protesting one another because they see democracy as under threat, engage in protest messages and actions that themselves threaten democracy. Protest is a foundational element of democracy – a human right in my opinion. But I a skeptical of these democratic protests that seem to hypocritically suggest we suspend democracy. 

We, the U.S., are in a state of historically unprecedented partisan division. Each side claims moral authority, and in doing so rules out the opinions and preferences of the other side as illegitimate and wrong.

This is not a formula for democracy nor a cohesive society. It eliminates the possibility of communication. And communication is one of two ways to resolve conflict – the other being domination (conflict, elimination of the opposing position).

A necessary condition for successful communication is that all opinions are valid. That each person has a legitimate right to have any opinion. That it is OK to disagree with each others’ opinions, but that it is not OK to claim moral authority that others’ opinions are wrong (nonsense, evil, unethical, stupid, insane, etc.). By ‘not OK’, I mean, not OK to be shared in a communicative setting, leading to communication breakdown and conflict (mobilization, domination, war, etc.).

If someone says that they think black people are dangerous, untrustworthy and criminals; or that women and men should fulfill certain roles in society and families as dictated by genes and chromosomes, or by God; or if they say that children should not be allowed to change gender, especially not take puberty-delaying drugs. I will only effectively communicate with them if recognize their opinions as valid and provide a safe space for them to share these opinions. To try and understand why they have them, and to seek a way forward that doesn’t require them to give up their opinion because I say so.

If someone says that black people have been systematically oppressed, that race is not an essential feature of human beings and is instead socially constructed; or that it is right for the government to promote equality; or that we should ignore gender in social life and treat men and women identically in all situations. I again will only make progress in communication when recognizing these opinions as valid and create a safe space for them to be shared.

If I am not willing to sit that my opinion has no ultimate truth to it, or that it is morally or intellectually superior to others’ opinions, I am not supporting democratic society, but instead my own vision of a society where I dictate opinion for others. That’s not democracy. So I question what we are doing with claiming when we claim that our position is right and others are wrong, and thinking that this is a democratic process.

Image AI credit

Questionable research practices from the practitioners’ perspectives

With Monica Gonzales-Marquez, Priya Silverstein and Eike Mark Rinke.

In preparation for Metascience 2025, I put together a panel with three other researchers called “Questionable Research Practices from the Perspective of the Researcher: Understanding Perverse Incentives using Autoethnography”. We used our own discussions in online meetings and individually as recorded narratives as content for this panel. The impetus was that much metascience points accusatory fingers at problems in science, thus supporting a culture of fear. Researchers comply to avoid scrutiny, and not necessarily out of an intrinsic motivation to do good science.

When successful and meaningful science is measured entirely by publication in a ‘high impact’ journal and high citation counts, the drive to do science is transmuted into a laser-like focus on publishing. The intrinsic motivation to contribute to the creation of scientific knowledge becomes confounded with publishing, while simultaneously deprioritising the robust, pedantic, methodical, humble labor involved in doing good research. The scientific method gets confounded with the mechanics of publishing and achieving “standing” in the scientific community.

We hope that by revealing our own experiences in the world of ‘publish-or-perish’, and how it has pushed us towards questionable research practices (QRPs), we might generate intrinsic motivation in others to dispassionately examine their own scientific practices.. By looking back through our histories in academic work and sharing them, we also expect to increase our own intrinsic motivations to be ever vigilant, and to learn to always privilege scientific integrity over publishing.

Confounding: Publication = Science

A major theme in our narratives is that science has been fully confounded with publishing. For some of us, the pressure to publish, and the toxic atmosphere and interactions pushed us out of academia entirely. For others, we internalized the publishing norm, convincing ourselves that we were doing impactful science, when we were actually just doing impactful publishing – which does almost nothing to alter collective human knowledge, promote social justice or solve other societal problems.

Some excerpts from our narratives:

Questionable Behaviors: Hacking

The pragmatics of doing science in a publish or perish culture is that we either engage in some (often undeliberate) hacking and storytelling, or are sanctioned.

Some excerpts:

Ego, Power and Personal Struggles

As human beings we are prone to seeking status, material security, community acknowledgement and different things depending on where we are at in life, and who we are personality-wise. Especially when in graduate school, we are in a position of little power in comparison to professors and our supervisors. People who  wield power over others, and push their own ego-centric agendas tend to be quite successful in science. This can lead to abuses of power, ego trips and other toxic behaviors that can diminish junior researchers’ ability to push back against pressures to engage in QRPs and have strongly demotivating effects.

Intrinsic Motivation

We hope that by speaking out, and normalizing scrutiny of our own experiences of questionable research practices as something valuable, we can help others become intrinsically motivated to do the same. Moreover, we propose that ethnographic narrative may be an underexplored but powerful method to help uncover the causes and motivations of questionable research practices from researchers’ lived experience of “doing science”.

Part of the motivation for this panel is based on one of the authors’ (Nate Breznau’s) own authoethnographic research into my QRPs. He presented preliminary results at the Sociological Science Conference at Cornell (link to slides).

Readers can find the full poster for our presentation at Metascience 2025 at University College London here.

Our future plans are to seek a larger sample of researchers and invite them to a semi-structured narrative sharing process (via recording themselves) to further study QRPs. In particular we hope to target early career researchers to shed light on the current state of science training and supervision experiences. 

The ISA should not ban scientific associations for failing to take political stances.

On June 29th, 2025, just one week before its World Forum in Rabat, Morocco, the International Sociological Association (ISA) leadership banned the Israeli Sociological Society (ISS).

The reason is that the ISS did not take “a clear position condemning the dramatic situation in Gaza.” In other words, that they are not taking a position in direct opposition to and protest of their own government. Although there are reasons that some would want to take such a position, in particular the incredible humanitarian crisis in Gaza, it is not fair to punish a scientific organization for not taking this position. In other words, not fair to try and force a scientific organization to mix itself in with politics. I can think of three concrete reasons.

  1. Science itself should prioritize inquiry, not political action. We conduct research and develop theories, that is science. Sociology is the science of society and social interaction. Therefore, sociologists should (continue to) engage with the war and humanitarian crisis unfolding in Gaza. They should try to present facts to answer the questions: What is happening there? Who is affected? Are the actions of the Israeli military in violation of the Geneva Convention? Is this a genocide? To blackmail other scientists to force them to take political action or face consequences to their participation in science and scientific exchange, runs counter to prioritization of inquiry in science.
  2. Scientists taking political positions could be in danger. In particular in authoritarian, or martial law settings, scientists who stand up against their government put not only their scientific enterprise at risk, but also their lives and their families’ lives potentially at risk. The ISA is demanding that members of the ISS take actions that could lead to their Society facing funding cuts or being cancelled altogether. Even worse, the ISA is demanding that Israeli sociologists take a position counter to their government in the middle of one of a radically politically charged historical moments – it is asking them to potentially put their lives as risk.
  3. Where do we draw the lines of where to take political action? If a humanitarian crisis and what appears to some scholars as a genocide are conditions for taking political action on part of the ISA, where do we draw the line? Why does the ISA not ban all Myanmar sociologists from participating because of the situation of the Rohingya? Perhaps even more perplexing are the odd exceptions: The ISS is banned, but what if an Israeli sociologist is not a member, can they participate? Perhaps someone forgot to pay their membership dues this year but was a member before. Can they participate? What about the United States and Germany? These are the greatest allies of Israel, why are they not banned? What about Russia invading Ukraine? These questions are rhetorical, because this is a Pandora’s box that is infinite and quickly digresses into everyone getting banned.

In fairness, there is a humanitarian disaster going on in Gaza. It is tragic for all the innocent bystanders swept up in it. Those who have been kidnapped, killed, harmed by bombing. Its awful to watch. But I am not writing this in regards to any political movement or government. I am writing this to defend science, which I see as my responsibility.

Democracy needs harmony first. Other issues second.

We live in a time of intense division. At least most of us perceive this to be true. Media is smeared with divisive messages, warnings and threats suggesting imminent destruction of society. Why? Because those terrible other groups are destroying it we are told.

Disagreement, discussion and conflict are productive for collective decision-making up to a point. But when emotions and conflicts escalate too far, discussion ends and war begins. Once sides become entrenched, progress comes to a halt and society is at risk of breakdown. The scenario of an internal war breaking out in a rich democratic society might seem far fetched, but keep in mind that it almost happened on Jan. 6th in the United States.

Even if an armed civil war does not happen, there are social, cultural and political wars already raging, especially on social media where the enemy is not seen face-to-face but consumed in the form of media messages and then constructed in our minds. I remember the first time I was accused of being part of a cultural Marxist conspiracy, because I was “woke”. What a wake up call.

Social harmony and integration of different perspectives must be the ultimate


It would be ideal if we could heal racism, sexism and other -isms. We in the behavioral and social sciences know how. This would simply require people across all ages to repeatedly throughout the course of their life to take courses and personal time to reflect deeply on their biases, discuss these biases with others who have same and different biases, and actively listen to others’ struggles with oppressive and prejudiced treatments (including the stories of white people feeling mistreated).

Now as a reader I ask you to briefly set aside your ideas about being “woke” or not, what you think is the correct moral imperative for society, and what you know is true about social injustice in the world. I ask you to instead be “awake” with me for a moment. Do you think a scenario where we all just give up our personal priorities and go into some kind of collective harmonic oppression therapy on a regular basis, where we heal from all forms of trauma and overcome social biases? Its ridiculously unrealistic.

People like me who have pushed for social justice have taken this for granted. Just because this would be an effective method to heal racial and other intersectional prejudices and injustices, does not mean it is possible. The general agenda of movements pushing for diversity, equity and inclusion has proven to be too unrealistic for the society we live in.

Read the statement again:

This would simply require people across all ages to repeatedly throughout the course of their life to take courses and personal time to reflect deeply on their biases, discuss these biases with others who have same and different biases, and actively listen to others’ struggles with oppressive and prejudiced treatments (including the stories of white people feeling mistreated).

This is what it would really take to move past the historical oppressions and traumas that are embedded in modern democratic societies, which were mostly founded on certain land owning men having democratic rights and everyone else beholden to them, with some used for slave labor. In other words, these issues run deep, they are historical, they have collective trauma and they required violence to maintain.

No campaign, no curriculum, and no ideology will ever fully convince most people that they are (a) racist at times and (b) that they need to do something about it. And those that are openly racist, certainly will not be willing to take any actions or support spending public money on it. And if you force them, they are even less willing to learn or change. I’m sorry to say, but most people have other things on their agendas. I wish this were not the case, but that’s reality.

This is a critical juncture. The movements pushing for diversity, equity and inclusion wanted to reduce group-based prejudice and discrimination. Not a bad goal in my opinion. These movements took many steps in that direction that could have helped achieve this goal, but it upset many segments of society. They went too far. There was a backlash.

There are still many people in the U.S. who believe that white families should own black slaves. And although such people are rare, it is likely that a majority of people racialized as white believe that persons with dark skin are genetically inferior. That they are essentially a single race with features that they are born with that make them less reliable and potentially dangerous. White people who think this way – the average white person I would guess whether they admit it or not – are triggered by social justice movements, especially those targeting race or gender identity. Especially if they themselves are struggling to make ends meet, telling them they are “the problem” is certain to piss them off.

I wish these things were not true, but this is reality. Such people are triggered and fueled by media fear-mongering playing up the threat of social justice movements. One outcome of this fear was a monumental swing in votes so that the Presidency, House and Senate all went Republican in 2024; and not just Republican Party but a certain brand of politics that is radically opposed to social justice movements that push for diversity, equity and inclusion. The first order of business was then not surprisingly to remove two decades worth of policies to push for social justice.

I say “critical juncture” , because society should go on. We must live together somehow, despite this reality. I say “must” because any other option would require removing certain groups from society. Trying to remove racist white men from society is just as racist as trying to oppress those with dark skin. We cannot remove racism through voting or arguing. It is a feature of society that nobody controls, it just exists.

Most of what I read online and hear from people is how awful things are. Those opposed to the social justice movements for diversity and inclusion see them as awful, and those who support the movements see those opposed as awful. Hate, hate and more hate. I cannot tell you how many of my close colleagues and friends, many with PhDs, constantly state that anyone that voted for the current U.S. president is disgusting, morally vacuous, and fucked in the head. They claim absolute moral superiority without discussion.

This reaction is so strong, that it appears to me that there is a complete lack of respect for other people, or understanding of why others voted the way they did. There is an unwillingness to discuss politics with such people on equal ground in a manner that respects their right to opine and vote for whomever they want. The groups tend to see the other as sick and/or morally inferior. If this remains or deepens in intensity, I will not be surprised if armed conflicts ensue, or perhaps worse, that we democratically vote ourselves into a dictatorship. Some kind of dystopian Hitler 2.0.


Social harmony and integration of different perspectives must be the ultimate priority in a democracy, and especially right now. The key decision-rule when deciding how to proceed in any policymaking or political discussions has to be to first accept and embrace others’ right to think and say how they feel, and vote for what they want. Otherwise, the only other option is war until one side is defeated and removed.

If we are to co-exist, it has become logically obvious to me that some issues that people deeply care about like abortion, public education, public health care, racism, sexism, transgender issues and more – many with profound moral stakes – have to take a backseat to the stability of the system as a whole. Without a stable system, it no longer matters whether abortion is legal because the end of society and democracy means mob rule. Mob rule is the end of the rule of law, where anything goes and people will become slaves of the most powerful gangs. Governance and democracy is there to prevent mob rule in the first place.

To suggest that the intensity of fighting for certain issues needs to be toned down, or to suggest that they might need to be shifted down the priority list is painful to write for me. I have spent my life deeply concerned about race and social justice. I studied African-American Studies and Sociology in college and Sociology and Political Science in graduate school and my focus has been on social inequality. I had not until recently considered that my ideas for society and policies might be simply impossible. If so, it is absurd and potentially dangerous to push for them.

What to do. I need to work on accepting and integrating the fact that people are racist and I (we) cannot change that much into my plans for what would be ideal policies and behaviors in society.

That does not mean prejudice and discrimination are not important nor morally wrong in my opinion, nor that abortion and other issues should not be discussed in society. It means instead that if pushing these issues too hard reduces social stability and harmony so much that society collapses, then they need to be set aside or at least reduced in intensity in the name of harmony.

Above: remains of a once vast and impressive society, governed by an early form of Democracy. Long laid to waste due to a lack of harmony.


Democracy is not based on agreement. It is also not based on cleansing society of those with opposing viewpoints. It works because people accept that others have different viewpoints, and that it is okay to disagree. It is a system designed to contain conflict, not to eliminate it. That means it only survives when tolerance and harmony are treated as priorities above ideological battles, moral righteousness, and sometimes social justice. We need to admit we disagree but that we are still going to work together despite that.

I personally would prefer to prioritize social justice, but if the cost is the end of democracy, I need to admit that it sometimes is not an option.

Racism exists, religious hatred exists, ridiculous fighting across political parties exists. But no solution to these problems is possible in a society that has collapsed under the weight of its own infighting. There is no justice in war. No equality in tyranny.

Putting harmony first is not a way of ignoring injustice, but as a way of preserving the conditions in which justice, tolerance and knowledge can grow and spread.


While global governance and international cooperation matter, we don’t have global governance that has any real power. The nation-state remains the most powerful unit in our world. It is where laws are made, where rights are protected or denied, where armies are mobilized, and where media and education systems are anchored.

If a country falls into civil war or dictatorship, nothing else matters. That’s why harmony must be cultivated within each country first. Stable nations become better neighbors. They are more predictable, more cooperative, and less aggressive. So harmony within nations becomes a foundation for peace between them.

Here is another case of certain priorities taking a back seat. It would be ideal if humans acted as one species, one society. If there was a system to enforce equal rights across the globe. But this doesn’t exist. And fighting for it in the modern world system is ineffectual and may lead one to be branded a terrorist. Then it is stupid to push for that in the first place. Impossible policy goals are a waste of time and dangerous. This is a reminder that we need to play the hand we were dealt. This is how things are. What can I do today to support harmony and co-existence. Me asking myself that led to this essay. Call it a manifesto for harmony as a solution if you like. Call it anti-woke. Call it selling out. I accept your criticism openly and would like to talk discuss it with you. And would like to do this as equals interested in co-existence.

Science in survival mode

Scientific research is unreliable. It comes with uncertainty. Whether launching a rocket or measuring racial prejudice, there is uncertainty. We use this uncertainty to make decisions. If the rocket has a 40% chance of exploding, best not to stick astronauts in it. If skin-tone bias of soccer referees is somewhere from none to a lot, it is irresponsible to conclude they are prejudiced.

Investigating uncertainty, is science. Truth and uncertainty are two sides of the same coin, they co-define each other. A problem for humans measuring uncertainty, is that humans are unreliable. Human scientists themselves add uncertainty to the measurement of uncertainty.

Recent studies suggest that somewhere between 25 and 60% of published statistical results cannot be recreated using the materials provided – these measures of uncertainty come with their own uncertainty. It turns out that scientific researchers are doing some really peculiar things to generate uncertainty. Some surveys suggest as many as 9% of scientists faked data at least once in their careers, and that more than half selectively reported findings – a behavior that makes the things they study to appear less uncertain than they actually are.

I believe the answer lies in their humanness. Like all animals, they are genetically programmed for survival. They are capable of both rational, reflective decision-making and split second reaction without any thought. Given time to reflect, a human would generally conclude murder is unethical, but simultaneously would not hesitate to kill if it prevented their child from being killed. Murder remains wrong, but to not kill and let a child die is also wrong. It would be irresponsible parenting failing to ensure survival.

Murder is a profound act when a human perceives themselves to be in a situation of life or death. It is a symptom of subconsciously activated defense mechanisms in survival mode. But there are many other symptoms. In order to avoid death, humans, like other primates can engage in deception, disassociation, aggression, manipulation, submission, scapegoating, theft and hoarding. If someone held me at gun point and told me to prove the earth was flat, I would have no problem doing it. The math would work, I would just need to fake a little data

Most scientists have highly valued knowledge and competencies and thus live in situations where their lives are not under threat. At least not as a result of their scientific practice. For the sake of this thought experiment, lets just rule out regularly occurring life-threatening danger as a cause of scientists exhibiting survival mode behaviors. This leaves the perception that they are under threat, as a possible explanation.

It takes only a few stimuli to induce survival mode behavioral changes in animals. Like hearing a frightful noise when seeing an animal. This animal and anything that looks like it become automatic sources of anxiety and fear even without the sound. Imagine being told over and over and over that you have to have an exciting study with powerful results in order to get an academic job after graduate school, otherwise you wasted 3-8 years of your life and probably a large chunk of capital on getting a PhD. Could this alone, without introducing any actual shocks or physical pain, induce Pavlovian fear? I encourage you to go ask any graduate student to answer this that does not yet have such a study.

Now imagine that during graduate school a student invests all their time and resources into an experiment. After it is complete, they hypothesized result, the one sure to be exciting and publishable, is not there. Imagine the horror, the shame, the feeling of failure, the panic. Remember the poor soul who leaped to his death because he misunderstood futures trading and thought he owed three-quarters of a million dollars? That was a triggered survival response. Because dying felt like the only way to ‘survive’ the horror of facing that debt. Imagine if he could have just changed the futures market by adding his own numbers to the market. Would he have done it?

We should not be surprised at all then, when scientists acting out of fear-based survival strategies, fake data. Diederik Stapel faked an entire career of data before being caught. His behavior was self-described as an “addiction”. A common reaction of individuals placed under fear stimuli that are emotionally damaging if not traumatic. The intense pressures and expectations of the academic environment created a context in which he felt compelled to engage in unethical practices to maintain his status and success. It does not make murder or data-faking right, but to not take this as grounds for indicting the scientific rewards system is certainly wrong.

The incentive structures have to change before we can honestly expect the fear-driven pressure to fake, cheat, lie or steal – in order to avoid the experience of loss associated with the common null results that occur when conducting high quality scientific research on radically complex human brains and societies that are frustratingly difficult to measure things in – to go away.

Amy Coney Barrett, Science and Democracy

Democracy is a system of governance that protects a society against tyranny. It prevents mob rule. It does so through rule of law. A democracy is only as strong as its legal enforcement mechanisms. Every democracy is imperfect, like the people living in it and governing it. But if laws hold firm, a democracy empowers its own citizens and guarantees their right to elect their own leaders, take part in politics and freely express their opinions.

US Constitution

The rule of law is not guaranteed. During times of war, democracies often experience marshal law, a suspension of some or all democratic rights. This also happens during emergencies like a massive economic collapse or domestic disturbance. As any AI or historian will tell you, rule of law is almost always removed due to actions from the executive branch of government. Executive orders that suspend democratic rule for some ‘valid’ reason, can lead to permanent suspension.

For example, economic depression hit Weimar Germany leading to executive orders suspending democratic rule, and an attempted coup in Turkey led to executive removal of many democratic institutions. The list is long. I recommend an AI prompt like “Give common reasons for the suspension of democratic rule either temporarily or permanently using as many historical examples as possible. Please categorize them by causes (e.g., war, economic shock, etc.)” for a quick history lesson.

The end of democracy is a new buzz concept in the United States. It became prominent in the media during the first Trump presidency. Media profit is driven by fear, and a potential end of democracy is freakishly scary. These fears ramped up dramatically after the attack on the U.S. Capitol on January 6th. The scariest part perhaps was that it was insinuated by President Trump, the leader of the executive branch. I have had conversations with many people, including extremely knowledgeable professors, that are riddled with these fears. On the other ‘side’ are radical media messages about a ‘cultural Marxist‘ takeover whose goal is ending democracy and replacing it with socialism, and this also promotes fear. Not only does fear sell media, it also motivates people to vote.

The direct attack on the U.S. capitol building could be seen many ways. Although attacking a U.S. government building is illegal, it had elements of protest and assembly that Americans understand as their Constitutional right. But even if we ‘excuse’ this event as a social movement, there are more recent efforts that could be seen as putting democracy at risk. The current executive orders to gut government is a move to consolidate power which is a key step in most previous successful ends of democratic rule. Trump also recently indicated he planned to declare protesting on college campuses illegal, a contradiction of the First Amendment right to free speech and assembly. So maybe these fears are founded. Maybe Trump wants to end democracy.

I see the question of whether Trump wants to end democracy as irrelevant in comparison to a more important question:

Although history teaches us that anything is possible, this seems unlikely. Consider that the judicial branch of government is responsible for the rule of law, and law is democracy. This makes the judiciary the primary protector of democracy. The top of this branch in the U.S. is the Supreme Court. Any action that ends democratic rule of law, would have to pass a Supreme Court ruling.

A judiciary is supposed to be a non-partisan branch, but there is a perception that this Court is increasingly partisan. A perception fueled by fear-mongering profit-hungry media cashing in on ideas that the U.S. is being taken over by ‘Nazis’ or ‘socialists’ or even ‘alien child molesters‘, and banking on the tiny share of people who seek chaos, to ‘burn everything down’ and ‘troll‘ social interactions online by tricking, heckling and shaming others.

Of course Justice’s have political ideologies and position themselves on certain sides of issues. They are citizens and humans. Their positions lead to interpretations of laws that tend to support their own ideologies. This was crystal clear in Dobbs v. Jackson Women’s Health Organization (2022), where the conservative majority overturned Roe v. Wade, in line with their anti-abortion ideologies. People, including justices and scientists, have bias. The question is:

Enter Amy Coney Barrett, the newest Supreme Court appointee and the deciding vote in the Roe v. Wade decision ending a woman’s right to have an abortion (the winning 5-4 vote would have been 4-5 if Ginsberg with her very different ideology had instead voted). Barrett is a Justice Republicans rave about for her extreme conservative ideology.

The Office of the President of the United States

Some see Barrett as a plant. That she will do whatever Trump wants.

Let’s pause for a second and consider the following: Ending democracy would violate at least three aspects of the Constitution that Barrett and every Justice have sworn to uphold: 1) direct elections of legislators, 2) indirect election of the executive through the Electoral College, and 3) it would likely take away freedom of speech and assembly. The third is an educated guess. We never had a non-democratic form of governance in modern human history that allows free speech and assembly.

What would lead a Justice to end democracy? They could be offered enormous personal gains. But these would be so high that it would be impossible to transfer such power and capital to them without being noticed and prosecuted as unethical and illegal for someone in a judicial role. Also, if they were offered an enormous sum of money or power, there is no guarantee they can keep it one democracy ends. Justices are highly educated persons, they would be able to make this rational calculation.

The other way a Justice would end democracy is on accident. Perhaps they pass laws whose consequences and risks are not clearly understood. The case of Citizens United v. FEC (2010) for example, ruled that citizens, corporations or special interests can spend unlimited money on political campaigns. This was argued by the majority as protecting free speech, that free spending on politics is equivalent to freedom of speech and expression. This has consequences that were unforeseen. For example, if other countries, corporations or persons combine their efforts they can have a larger influence on U.S. politics than any American or American interest. For enemies of the U.S., this ruling is a legal pass to undermine American politics, business interests and power.

This is where the importance of science comes in. Science means “a systematically organized body of knowledge on a particular subject” (Oxford Online Dictionary). Every judge, clerk and lawyer is a scientist by this definition. They systematically investigate and understand the laws of the United States. This requires usually 7 or more years of studying law and passing an exam to earn the right to practice law. Every Supreme Court Justice is a scientist of law. We often do not use the word “science” when talking about them, but that is what they do. They work with systematic, organized and historical knowledge of laws, events and how the two are related.

Amy Coney Barrett seems to have little experience practicing law. She was a Professor of Law at Notre Dame for over 15 years, but has only 3 years experience working at a law office and some clerking experience at the beginning of her career. But a lawyer and a professor of law do much the same things. Both have to know their subject deeply and then make arguments about it. The lawyer tries to win a case and the professor tries to publish scientific articles and books, they do so through logical argumentation based on their research.

To argue a case or decide on a case requires a scientific investigation of the law. In Roper v. Simmons (2005), it was necessary to understand the neuroscience of adolescent brain development. Because juveniles have less impulse control and decision-making capacity, executing them is cruel and unusual punishment. The better scientific research that justices do, the better the chance that the Constitution will be protected. If they are sloppy and do not do enough scientific research, that is when they risk unforeseen consequences like with Citizens United v. FEC (2010) which introduces a serious security risk to the government of the United States.

Although Justices may be of different opinions about how much political power is allowable to single individuals or corporations, they would all agree that if all of the power were in the hands of one individual that it would no longer be a democracy – a word that literally means decision making power in the hands of the people rather than just one of them.

This makes Amy Coney Barrett among the fittest defenders of democracy on the court. She and John Roberts are extremely experienced scientists of law. Roberts was the editor of the Harvard Law Review, an academic journal, one that is based on scientific research, analysis and interpretation of law. He has the most practical experience of any Justice and when he worked as a lawyer he won 25 of 39 cases argued before the Supreme Court. To do that, he had to do extensive research and construct logical (scientific) arguments based on this research.

Barrett on the other hand has less ‘practical’ experience. But look at her CV or read one of her science of law publications. She spent just as much time as Roberts (adjusted for her age) analyzing law, systematically developing a knowledge and logical interpretation of law. This is what every scientist does and what ideally every person in a judicial career does. The stronger their scientific work, the better the protection of the laws themselves, and the lower the risks a ruling will introduce a threat to democracy. For this reason, science is at the center of protecting democracy. Science has to be a priority if the U.S. is to remain a democracy.

Whether abortion is legal or not will not matter anymore if the United States becomes an autocracy like Russia. The right to argue and fight for our preferences regarding race, money in politics, intergenerational trauma resulting from several wars and a system of slavery, abortion, or whether felons have the right to vote are luxuries that we have because we are a democracy, and we will remain a democracy if we prioritize science. Amy Coney Barrett is a scientist, and when she follows her scientific practices, will not vote to end democracy. This puts her in a crucial role in case Trump does have such intentions.

Barrett already has upheld the sanctity of contracts in Trump’s attempt to refuse to pay government workers. Contracts are a crucial aspect of democracy which cannot function if any party is free to void a preexisting contract. For me, this is evidence of her scientific, legal mind.

It should prevail regardless of my lack of agreement with her conservative ideology.

The Gilmore Car Museum, A Cultural Institution

A recent visit to The Gilmore Car Museum in Hickory Corners, Southwest Michigan helped me reflect on the significance of collecting. Not just cars, but collecting in general. It is a ritualized-yet-personal, cultural-yet-individual, social activity. This trip was a reminder that collecting is more than a pastime. It involves history, nostalgia, gregariousness and fun.

Collecting things is as old as human history. Six-hundred years before the start of the Christian calendar, King Ashurbanipal built a monumental collection of texts – probably the largest library ever at that time. Four-hundred years before that, Zhou Dynasty rulers collected valuable artistic items that are still sought after today sometimes carrying multi-million dollar price tags.

A collector is someone who “collects things of a specified type” according to the Oxford Dictionary. But the act of collecting is far more than assembling a collection of certain objects. It is a practice of expression, social participation, personal identity formation, and collective cultural development (Dillion 2019). Social scientists observe similar traits and behaviors across collectors of everything from stamps and coins to hair from famous people to magnets to cars.

The Gilmore Car Museum is a profound testament to the connections between collecting, nostalgia, and history. It houses over 400 vehicles across a 90-acre parcel of land, some more than 100 years old. It simultaneously showcases automotive evolution and the depth and breadth of cultural and emotional narratives that these machines embody. For many enthusiasts, classic and modern cars are expressions of specific styles and historical eras, linked to personal and collective memories. Collective memories are the foundations of culture in society. Cars are culture.

Photo credit: Time Magazine

A Baby Boomer might personally remember turning 17, borrowing the family’s 1962 Chevy Bel Air and taking a date to the drive-in movies for the first time. The same every day, yet iconic car brings to mind collective memories of post-war economic boom, and infrastructural planning prioritizing driving automobiles over all other modes of transportation, ultimately turning America into what Cotton Seiler calls a ‘Republic of Drivers’. Although this took place in most other industrializing countries of the time, the vast amounts of land in the U.S. available for building roads, diners, dealerships, drive-ins and car-enthusiast-get-togethers is truly exceptional.

The act of collecting vehicles often stems from a desire to preserve, relive and celebrate the past. Collectors of almost all things tend to have this desire. Whether it’s a cherished family car from one’s childhood or an iconic model that encapsulates a specific era of American life – cars are memories. Ken Fischang, Director of Commercial Operations, and himself a collector is no exception:

This statement captures the linkages between Ken’s own childhood, culture of his early years and specific car brands such as a Cadillac. The Gilmore Car Museum itself reflects the nexus of all things collecting and collectors. Cars trigger sensations such as feelings of freedom, technological ingenuity, and cultural identity. These sensations are collective. Collectors thrive in communities, sharing their knowledge and treasures at car shows or rallies, or at the Gilmore Car Museum, where the camaraderie is as cherished as the chrome.

Through its extensive collection and thoughtfully curated exhibits, the Gilmore Car Museum offers visitors an immersive journey into the intertwined stories of automotive innovation, personal nostalgia, and historical context. This place is a destination rarity with a near perfect 4.9 score in Google Maps with 4,400+ reviews as of Dec. 4th, 2024 when I last visited.

It also provides collectors such as Bill (Museum Trustee President) and Barbara Parfet a place to showcase their cars. This is something the collectors I know love. What good is a rare, prized item locked in a basement somewhere? Unless that basement protects it from pillaging! Visitors to the Museum can see a car on loan from The Parfets’ collection: A 1938 Mercedes-Benz 540K Sports Tourer that is one of two ever produced. The other was destroyed in the chaos in Germany. The first owner of this car in Dresden, (former Eastern) Germany hid it in a garage during WWII, buried the garage underground and planted a rose garden above it. It was unearthed safely only 60 years later after the fall of the Wall and German Reunification.

Photo credit: Judith Offerhaus

Visitors to the museum who are either not car collectors or cannot afford to buy cars for collection purposes, still can become collectors when experiencing cars and their stories. Like this incredible and immaculately restored Benz. To be a collector one only has to collect linkages between this car, what they or their parents may have experienced related to WWII, and the cultural importance of cars in their minds. Collecting does not require a material collection – it can take place entirely in our imagination, culture and fantasies. It is an experience and attitude.

Children regularly play the game ‘if you could have any car, what would it be?’. For me as a child it would have been a DMC Delorean, their role as a time travelling machine in the movie Back To The Future permanently imprinted their value and ‘coolness’ in my mind. Even though I understand these were poorly designed and underpowered as cars, they are high performing and limitless as symbols. When I view them, they embody the potential for technological and cultural advancement, if not space-time exploration. The childish thrill I get seeing a Delorean reminds me of kids who often collect toy cars, like Matchbox cars. This is in principle no different than collecting real cars. They are toys, but they are the experience of collecting. Each kid has their favorites, each kid imagines driving them someday, having a garage… maybe racing.

For car collectors, each vehicle is a machine and a story. It is carefully preserved with meticulous attention to detail. Their garages are often filled with memorabilia linked to the specific cars they own. They want things in original, or restored to be identical to original form, to preserve the storytelling that links their own existence and cars to wider American or global life and meaning. Or, they want original everyday cars that are customized to be one of a kind – variants of something making them rarer and more valuable. Just ask any comic book collector about ‘variants’ if you want to delve into the esoteric and eclectic mores of collecting.

The experience of collecting as something beyond any person or item is visceral at the Gilmore Car Museum. It is impossible to pass through without engaging in the passion of car collecting by chatting with employees or other visitors, reading the signage, and immersing in exhibits of cars placed amidst historically relevant artifacts.

The Car Museum puts on a Winter Wonderland Event each year that brings the linkages of nostalgia and collecting further into focus. Each decade since 1900 is showcased, usually next to a car from that era. In each there is a Christmas tree decorated with ornaments, toy gifts below it, and with a radio or television nearby all presenting artifacts and media from that time. Visitors can experience how Raggedy Ann was to the 1920s what the first Nintendo system was to the 1980s. 

Photo credit: Judith Offerhaus

In addition to providing the chance to ride in vintage cars through a Candy Cane Lane complete with Christmas music and light show, the Christmas event at the Gilmore Car Museum is a further reminder that collectors’ vision extends far beyond the present. They see themselves as guardians of a legacy, ensuring that vehicles and their stories remain vibrant for future generations and that the experience is fun if not thrilling. Their craft is an intricate dance of nostalgia, history, and dedication, making them not just enthusiasts but stewards of the general spirit of automotives and the role of cars throughout history.

Header photo credit: Judith Offerhaus

Open Science? What’s That?

Was the answer I got from Andrew Abbott at a Hogwart’s dinner I was fortunate enough to attend a few years back when I asked him after dinner, “what are your thoughts on open science?”

That’s right. Open science. Say something paradigm changing.

We have so many results and coherent arguments about the status of science. To make an impact here requires synthesizing them into something new. Not purely constructively new in nature, but also subtly undermining all that came before. Something was missing all along and this new perspective is it. That’s the problem.

Our formula for declaring theory and opinion in scientific writing is that it must be big. Paradigm impacting. Makes you think. Expresses the scientist author’s authoritative intellect and ingenious perspective. With seemingly effortless written words, the scientist author lays down an argument so convincing, that it must be true. It was true all along. It is so obvious, why didn’t I see it? Thinks the average reader.

If Diederik Stapel was never caught, he would be a model for success. THE model for success in social psych. You don’t know what the formula for success is? Ask an economist. You have 3, maybe 5 journals in which you need to get published very early on. You have to have a job market paper that is known. You have to have connections. Otherwise you are on the sidelines, building an exit strategy. This is roughly true in the other social and behavioral science disciplines, yet we in sociology or political science will mostly deny it. We are better than those status maniacs.

But it is publish-or-perish. And this is the model.

To do publish, requires word wizardy. The ability to take any findings and craft an argument about them that makes them seem relevant is more valuable than research method skills. To make any findings seem essential to our fundamental understandings of somethings that are important. That is how we do science. Framing.

Andrew Abbott has the goal of out thinking, reading, writing and arguing everyone he encounters. Ask him if you ever meet him. He will not deny this. Easily considered by most to be one of the greatest living sociologists. He was editor of AJS and published book reviews of random, late-night selections in the Oxford library under a pen name, because it was fun. Because that is what legends do, and then tell about. He once wrote that sharing all material to be reproducible and replicable would make science boring.

As long as this is the model for success, we are not making real progress.

I sat at a dinner once with Ronald Inglehart. Every student, postdoc and professor there gawked with wide eyes. What was so appealing and enthralling. Why was this scientist a rock star for us? Was it his findings? Or was it his status? I think status. I think that is what scientists are seeking. I observe it. I feel it. If you have to have certain publications in order to not just pursue a scientific career, but to be at the ‘forefront’ of a subfield, then does it really matter what is in those publications? As long as you get to claim that status, that is it. You win. Why do athletes take steroids, cheat? Is it because of their great passion for the sport? Or is it to win status?

I entered a Master’s program in order to learn about the problems associated with a topic that was pressing for social justice and democracy. I still want to do this. But status was dangled in front of my eyes. ‘Publications are the currency of our trade’. The professor who told me this was absolutely correct. Now I want to do open science, but I have to perform wizardy with words to be recognized as central to the open science movement. This makes me uneasy to the point of nausea.

How can science become trustworthy and effective, if we have to be witty, cutthroat and/or famous in order to take part in it? How can we overcome this delusion? When will we stop and say enough is enough?

This type of honesty is what John Levi Martin specifically advised not to put out on the intrawebs because it will come back to bite me someday. Image (presentation) is everything. Warhol or Abbott, they figured that out long ago. I guess then for my own peace of mind, I extend my hand. Bite away you biters.

Human Induced Climate Change

Show me the data


Nate Breznau

A quick guide to human impact on the environment

Carbon Emissions

The amount of Carbon we’ve released into the atmosphere is nearly perfectly collinear with the Industrial Revolution and subsequent industrial growth. This is easily estimated from ice samples.

Carbon is unquestionably a result of human activity

We can measure random and cyclical carbon in the atmosphere from ice core samples. Never in 800,000 years have we seen carbon levels like those since the Industrial Revolution.

source

Climate Warming & Emissions

A striking correlation between emissions and global average temperatures. Although temperatures fluctuate regularly, their fluctuations track upward following human-based carbon emissions. 

source

Microplastics

These are only produced by human production. Note that there are several parts of the ocean where we find more than 10 pieces per cubic meter (dark purple circles and dark red stars). There are over 1.3 billion cubic meters of surface water in the ocean. 

source

Polar Ice Degradation

We do not have accurate data from long ago, but since 1990 the arctic ice shelves have declined in size dramatically. This also leads to a rise in sea levels. Here a comparison of ice in summer and winter in 1990 compared to 2020.

source

Scientific Consensus as of 2019

A review of all published climate science studies in peer reviewed journal articles between 2005 and 2015 suggested a consensus of somewhere between 83 and 97% of climate scientists that humans were causing the major changes we observe in our environment in the last 100 years1

A new study of all of the just over 11 thousand articles across all disciplines and journals published in 2019 revealed that this consensus was 100% (within a natural margin of error)2

Bibliography

1. Cook, J. et al. Consensus on consensus: a synthesis of consensus estimates on human-caused global warming. Environ. Res. Lett. 11, 048002 (2016).

2. Powell, J. Scientists Reach 100% Consensus on Anthropogenic Global Warming. Bulletin of Science, Technology & Society 37, 183–184 (2017).

A fake journal, algorithmic plagiarism and tricking Google Scholar

A case of a fake journal that passed Google Scholars’ bots and algorithm checking to become indexed. The journal contained articles that were completely plagiarized but assigned new titles, abstracts and authors.

See updates at end of blog for table of plagiarized papers, and a small win for open science after the domain host revoked the content of the plagiarized paper’s URLs.

A colleague of mine was searching Google Scholar using certain words and the number two hit appeared with the following title and direct download link.

The PDF looks like a journal article. The journal has an ISBN instead of DOIs, but this is not unheard of. It even has an impact factor listed: 7.11, it must be good.

It turns out that this seemingly interesting paper with an alleged author who is an expert in this area, is plagiarized.

How do I know it is plagiarized? Just ask the real authors of the paper which appeared with a different title and abstract in Social Science Research one year earlier. Oh and with an identical word-for-word text, with some mistakes relating to equations – possibly a failed scraping algorithm.

The journal title is Journal of Current Research and Review, but it is extremely difficult to find through an internet search. This requires endless scrolling (as of 10.08.2023), or removing the ending on the download URL to arrive at the index page.

Scrolling down on the home page reveals the “Latest Articles”. Here I find the same title that appeared in Google Scholar. However there is no download link, only the title and abstract. In other words, the URL that Google Scholar found to download the paper is not accessible through any link on the journal’s website.

How is it possible that Google Scholar would pick up and index such garbage?

I asked ChatGPT to tell me how Google Scholar works, and the results suggest that someone or something had advanced knowledge and programming skills that would provide the right meta-data and materials on the journal webpage to pass the automated Google Scholar bots which are constantly scraping the web for academic papers and publications.

From ChatGPT prompted with “How does a journal get indexed in Google Scholar?” (09.08.2023). I received the following answer. Note that I removed the descriptions of each point, as they are mostly self-evident.

Google Scholar indexes scholarly literature, including journal articles, conference papers, theses, and other academic resources. In order for a journal to get indexed in Google Scholar, there are several steps and criteria to consider:

  1. Quality Content
  2. Digital Format
  3. Open Access and Accessibility
  4. Consistent Publishing Schedule
  5. Meta Tags and Structured Data
  6. Proper Article Metadata
  7. Citations and References
  8. Author Profiles

It’s important to note that Google Scholar’s indexing process is automated, and there is no formal application process for journals to get indexed. Google Scholar’s algorithms discover and index content based on various factors. However, journals can follow best practices to increase their chances of being indexed and improving their visibility within Google Scholar’s search results.

The journal website and meta-data had to pass Google Scholar’s algorithm. This is no lucky feat. Although Google does not reveal any usage of AI to evaluate journals for indexing, it clearly has an advanced system of scraping, filtering and crawling. Who or whatever designed this journal, or really this website that looks like a journal, understood how to pass the test. For example, the “Latest Articles” appear to be from “Volume 14” suggesting to potential crawling bots that the journal may have been published for 14 years now.

This is of course false. There are no other articles readily accessible from the journal’s website other than the two that appear. The other article is also plagiarized, in case you were wondering. But, a closer look at the URL reveals that the article I am discussing has the number “800” at the end of its URL.

https://zapjournals.com/Journals/index.php/jcrr/article/view/800

Changing this number yields other papers. At least as far back as number 795; prior to that yields 404 errors. Moreover, I cannot find the papers from 795-798, only their titles and abstracts.

The journal website’s main page yields something even stranger. It looks like what a computer science student might create as an example page to try and sell as a template or to demonstrate the services on offer for a website construction gig. The content has absolutely nothing to do with journals, academia or publishing.

It even comes with fake testimonials. I wonder whose pictures were stolen for this…

The real question for me is, ‘what to do about this?’. Clearly this is fraud and constitutes both ethical and legal infringements on science. Looking up the host of the domain of the journal using ICANN reveals that it is provided by a Lithuanian company Hostinger.

Looking at this company’s website suggests they are legitimate. The provide contact information to report abuse. So that is what I did by sending them the info in this blog post.

Hostinger replied within two days and told me they take abuse of their services very seriously and asked me for more evidence so they could pursue the case. I gave them the two links for the plagiarized papers and their original versions published one year earlier in Social Science Research.

https://zapjournals.com/Journals/index.php/jcrr/article/download/799/1237

https://zapjournals.com/Journals/index.php/jcrr/article/download/800/1238

Word for word plagiarized from:

https://www.sciencedirect.com/science/article/pii/S0049089X22000321

https://www.sciencedirect.com/science/article/pii/S0049089X22000333

For all the negative publicity surrounding Elsevier, they are still a player in the academic world. In 2020 they owned roughly 16% of the academic publishing market. If there is anyone who would want to prevent abuse of their services, including plagiarism of their work, it is Elsevier. They have a trove of lawyers fighting and winning battles to protect their content across the world. As the two plagiarized papers that I can download are from the Elsevier journal Social Science Research, it made sense to contact them as well. Elsevier’s due process suggests contacting the journal editor first. Thus, I have sent this information to the lead editor.

The journal also lists an ISBN number. But this number is a fake, and returns an invalid search with the ISBN lookup tool.

Google certainly would not want its products indexing fake journals with plagiarized papers, so I took the liberty of contacting them as well.

What is striking about the journal is that they have a long editorial board list. Internet searching reveals that these are real scholars. I also contacted them. I will continue to report on this case as it unfolds.

The question for me is: ‘what motivated someone to create this site?’. There is clearly no profit associated with it. There is also no status gain, because the plagiarized papers have authors assigned to them who are not the original authors and clearly not players supporting the fake journal. They are highly established scholars in their respective sub-fields. These fakely assigned authors are perfect examples of what an AI might choose to assign to a certain topic. I tested this by asking Chat GPT if Seamus McGuinness could have written the abstract. The response points at AI as a source for potentially re-writing abstracts of the plagiarized papers and for finding suitable authors to assign to them. ChatGPT said:

Yes, Seamus McGuinness could be a potential author who might have written this abstract. Seamus McGuinness is known for his research on labor market issues, including education-job mismatches, gender disparities, and remote work. His expertise aligns with the themes discussed in the abstract, making him a plausible candidate as one of the authors who could have written it.

It remains a mystery for now what is up with this website and the fake journal. Was it a computer science project that accidentally got picked up by Scholar, one that was never intended for public consumption? Was it an attempt to create a journal, but try and hide the real content of the journal until it could pick up real submissions? Was it entirely AI generated, to showcase the power of an AI?

[Update 14/08/2023]

Thanks to Random Cat on Twitter, I learned that there are more papers than just two. Using Google Scholar they searched by journal.

This allowed me to compile a table of plagiarized papers.

Table of Plagiarized Papers in JCRR, found via Google Scholar

Original TitleOriginal Author(s)Original JournalJCRR TitleJCRR AuthorsLink to Original ArticleLink to Plagiarized Article
The motherhood wage gap and trade-offs between family and work: A test of compensating wage differentialsNick Wuestenenk & Katia BegallSocial Science ResearchCOMPENSATING WAGE DIFFERENTIALS AND THE MOTHERHOOD WAGE GAP: A COMPARATIVE ANALYSISHK Kleven & CL Landaislinklink
Conflicting signals: Exploring the socioeconomic implications of gender discordant namesAndrew Francis-Tan & Aliya SapersteinSocial Science ResearchBREAKING BOUNDARIES: EXAMINING THE INTERSECTION OF GENDER DISCORDANT NAMES AND SOCIOECONOMIC ATTAINMENTAL Roberts & M Rosariolinklink
Gender overeducation gap in the digital age: Can spatial flexibility through working from home close the gap?Ana Santiago-Vela & Alexandra MergenerSocial Science ResearchBRIDGING THE GENDER OVEREDUCATION GAP: EXPLORING THE ROLE OF WORKING FROM HOME IN THE DIGITAL ERASMG McGuinnesslinklink
How the Great Recession changed class inequality: Evidence from 23 European countriesJad MoawadSocial Science ResearchCLASS INEQUALITIES IN THE WAKE OF THE GREAT RECESSION: A STUDY OF 23 EUROPEAN COUNTRIESPD Allisonlinklink
Higher education and high-wage gender inequalityNatasha Quadlin, Tom VanHeuvelen & Caitlin E. AhearnSocial Science ResearchASSESSING THE CONTRIBUTION OF EDUCATION TO GENDER WAGE DISPARITIES IN HIGH-EARNING PROFESSIONSJ Jacobslinklink
TRANSFORMING RESEARCH: EXPLORING THE INTERPLAY OF DATA MINING, MACHINE LEARNING, AND KNOWLEDGE DISCOVERRobert M. Bond & Christopher J. FarissSocial Science ResearchsKnowledge Discovery: Methods from data mining and machine learning [b]Xiaoling Shu & Yiwan Yelinklink
SOCIAL MEDIA ADDICTION: PROPOSED INDICATORS AND STAGESZakaria I. Saleh & Omar Zakaria SalehInternational Journal in Commerce, IT and Social SciencesUNPACKING THE CYCLE OF SOCIAL MEDIA ADDICTION: UNDERSTANDING SYMPTOMS, PROGRESSION, AND RECOVERY [a]OZ Salehlinklink

[a] Published twice in JCRR with different authors

[b] Not indexed in Google Scholar

All are from Social Science Research except one. There are also other papers where I cannot find an original. These papers might actually be original papers, or stolen working papers that are not easy to find online.

Interestingly, some papers that appear that they may not have been plagiarized have a different display in PDF form. This includes contact details for the journal. I thus emailed them as well to inform them that they are committing ethical and legal fraud.

[Update 23.08.2023]

Hostinger investigated the reported problem and determined that the user of their domain had violated the terms of ethical/legal usage and removed the plagiarized papers. However, the journal website still appears. In other words, a journal that obviously intentionally plagiarized several articles, possibly to boost its reputation and encourage others to submit and thus pay the 45 dollar fee, is still out there lurking. Moreover, this journal is part of a larger company called Zenodo Publishing. On their main page the list dozens of journals. If I had to guess I would assume these journals are also predatory, and may contain plagiarized content. Only investigating this will prove if this is true.

More to come.

ChatGPT teaches open science

I asked ChatGPT to talk about open science. This post is the unabridged conversation. ChatGPT is a trained algorithm using machine learning. It is what most would refer to as Artificial Intelligence. And intelligent it apparently is.

~

Nate: I am teaching a course on open science. Could you summarize the key arguments of the open science movement?

ChatGPT: Certainly! The open science movement aims to promote greater transparency, collaboration, and accessibility in scientific research. Here are some key arguments and principles of the movement:

  1. Open access to scientific publications: Advocates for open science argue that research findings should be made freely available to everyone, rather than locked behind paywalls or subscription fees. This allows for wider dissemination of knowledge, which can benefit both researchers and the general public.
  2. Data sharing: Open science also emphasizes the importance of sharing data and methods, allowing others to reproduce and build upon research findings. This can improve the quality of research and promote greater collaboration across scientific disciplines.
  3. Reproducibility and transparency: Open science advocates for greater transparency in research methods and data analysis, as well as reproducibility of findings. This can help to prevent fraud and errors in scientific research, and can promote greater trust in scientific results.
  4. Collaborative and participatory science: Open science encourages collaboration among researchers, as well as engagement with stakeholders and the public. This can help to ensure that research is more relevant and useful to society, and can promote greater accountability and transparency in the research process.
  5. Open source and open tools: Open science also advocates for the use of open-source software and tools, which can be freely accessed and modified by others. This can improve the reproducibility and efficiency of scientific research, and can promote greater collaboration and innovation.

Overall, the open science movement seeks to transform scientific research into a more transparent, accessible, and collaborative endeavor, with the ultimate goal of advancing knowledge and benefiting society.

Nate: As I said above, I am teaching a course on open science. It is targeted at university students. Could you recommend some introductory readings on this topic?

ChatGPT: Certainly! Here are a few introductory readings on open science that you may find helpful for your course:

  1. “The Open Science Training Handbook” by the Open Science MOOC: This is a comprehensive guide to open science, covering topics such as open research data, open access publishing, and reproducible research. The guide includes both theoretical and practical advice, as well as case studies and examples.
  2. “Ten Simple Rules for Reproducible Computational Research” by Sandve et al. (2013): This article provides a set of guidelines for ensuring that research is reproducible, including recommendations for version control, documentation, and sharing data and code.
  3. “Open Science: A Very Short Introduction” by Ana Brandusescu (2020): This short book provides an accessible introduction to open science, covering the history and principles of the movement, as well as key topics such as open access publishing and citizen science.
  4. “Open Science Framework (OSF): A Tutorial” by the Center for Open Science: This tutorial provides an overview of the Open Science Framework (OSF), a free and open platform for managing research projects and sharing data and code. The tutorial covers the basics of setting up a project on the OSF, as well as using the platform to collaborate with others and share research outputs.
  5. “The FAIR Guiding Principles for scientific data management and stewardship” by Wilkinson et al. (2016): This article presents a set of guidelines for ensuring that research data is Findable, Accessible, Interoperable, and Reusable (FAIR). The principles provide a framework for managing research data in a way that promotes openness and reusability.

These are just a few examples of the many resources available on open science. Depending on the focus of your course, you may also want to look for readings on specific topics such as open access publishing, preprints, or data sharing.

Nate: What are some actions students can take during their studies that will make their research more open? Open, as in more aligned with the goals of open science.

ChatGPT: There are several actions that students can take during their studies to make their research more open and aligned with the goals of open science. Here are a few suggestions:

  1. Share research data: One of the key principles of open science is making research data openly available. Students can make their research more open by sharing their data in a public repository, such as the Open Science Framework or Zenodo, and including a data sharing statement in their publications. Students can also share their data with collaborators and other researchers upon request.
  2. Use open source software: Students can use open source software, such as R or Python, for their data analysis and statistical modeling. Open source software is freely available, and its code can be accessed, modified, and shared by anyone, which promotes transparency and reproducibility in research.
  3. Practice pre-registration: Pre-registration involves submitting a detailed description of the research design, methods, and analysis plan prior to conducting the study. This can help prevent bias and increase transparency in research. Students can pre-register their research on platforms such as the Open Science Framework or AsPredicted.
  4. Use open access publishing: Open access publishing makes research articles freely available to anyone, which can increase the visibility and impact of the research. Students can publish their research in open access journals or deposit their articles in open access repositories, such as arXiv or PubMed Central.
  5. Collaborate openly: Collaboration is an important aspect of open science, and students can promote openness in their collaborations by sharing their research questions, data, and analysis plans with their collaborators. Students can also use collaborative platforms, such as the Open Science Framework or GitHub, to share and manage their research projects.

These are just a few examples of actions that students can take to make their research more open and aligned with the goals of open science. By promoting transparency, reproducibility, and openness in their research practices, students can help advance the principles of open science and contribute to a more open and collaborative research culture.

Nate: Are there arguments against practicing open science? If yes, where do these arguments come from?

ChatGPT: Yes, there are arguments against practicing open science, although they are often debated and may not be universally accepted. Some of the main arguments against open science are:

  1. Lack of incentives: Critics argue that there are few incentives for researchers to engage in open science, particularly when it comes to sharing data or making research available in open access. For example, researchers may be concerned about the impact of open sharing on their ability to publish in high-impact journals, obtain grant funding, or advance their careers. These concerns may be particularly acute for early-career researchers, who may be under pressure to produce high-profile publications and may not have the resources or institutional support to engage in open science practices.
  2. Risk of misuse: Some researchers argue that open science practices, particularly around sharing data, may increase the risk of misuse of research results. For example, in some fields, sharing data could lead to concerns about confidentiality, privacy, or intellectual property. Researchers may be concerned about how their data will be used, particularly if it could be used for purposes they did not intend, or if it could be used to generate findings that are inconsistent with the original research.
  3. Cost and logistics: Practicing open science requires resources, particularly around data management, sharing, and archiving. These costs can be particularly high for large or complex datasets, which may require specialized tools or infrastructure. Researchers may also face logistical challenges around sharing and storing data, particularly if they are working with sensitive or proprietary data.
  4. Misinterpretation or misrepresentation: Some researchers argue that open science practices may increase the risk of misinterpretation or misrepresentation of research results. For example, if research results are shared before they have been properly vetted, this could lead to the spread of inaccurate or incomplete findings. Researchers may also be concerned about the potential for misrepresentation of their work, particularly if their research is taken out of context or used for purposes they did not intend.

These arguments against open science are often debated, and there are counter-arguments and efforts to address some of the concerns raised. However, it is important for researchers to be aware of the potential challenges and risks associated with open science practices, and to carefully consider how to balance the benefits and costs of openness in their own research.

Nate: Can you recommend any readings that are highly critical of open science?

ChatGPT: While there are some critiques and debates around various aspects of open science, it is important to note that these are often specific to certain aspects of open science and not necessarily a wholesale rejection of the movement. However, here are a few examples of critical perspectives on certain aspects of open science:

  1. “Against Transparency” by Carl Elliott: In this article published in The Chronicle of Higher Education in 2014, Elliott argues that the push for transparency in research can lead to a culture of surveillance and mistrust. He suggests that the emphasis on open access to data and methods may create an environment where researchers feel they are being constantly watched and evaluated, which can ultimately have negative effects on scientific creativity and collaboration.
  2. “Why the ‘Open’ Philosophy Can Harm Science” by Thomas Basbøll: In this blog post published in 2018, Basbøll argues that the open science movement can create unrealistic expectations for research, particularly when it comes to the idea of “openness” as an unqualified good. He suggests that the focus on openness can lead to a neglect of other important values in science, such as quality, relevance, and creativity.
  3. “The Five Stages of Open Science Grief” by Cameron Neylon: In this blog post published in 2014, Neylon describes his experience of grappling with the challenges and limitations of open science, particularly around issues of data sharing and reuse. He suggests that the open science movement can sometimes oversimplify complex issues and create unrealistic expectations for what openness can achieve.

It is important to note that these are just a few examples of critical perspectives on open science, and that there are also many scholars and researchers who support the principles and practices of open science. As with any complex and rapidly evolving movement, there are likely to be ongoing debates and discussions about the benefits and challenges of open science, and it is important for researchers to engage critically with these issues and develop their own perspectives.

Nate: Do you think AI can improve the implementation of open science practices? If yes, how?

ChatGPT: Yes, I believe that AI (artificial intelligence) can potentially improve the implementation of open science practices in a number of ways. Here are a few examples:

  1. Data management and sharing: AI can be used to help manage and share research data in a more efficient and effective way. For example, AI tools can help automate data cleaning and formatting, making it easier for researchers to share data in a usable format. AI can also be used to help identify sensitive or confidential data that may need to be redacted or protected before sharing.
  2. Open access: AI can help increase access to research by providing tools for automatic translation and summarization. For example, an AI tool could automatically translate an article written in one language into another language, making it accessible to a broader audience. AI can also be used to summarize articles or research papers, making it easier for readers to quickly understand the key findings and implications.
  3. Peer review: AI can be used to help improve the efficiency and quality of peer review. For example, AI tools can be used to automatically screen articles for plagiarism or identify potential conflicts of interest among reviewers. AI can also be used to help identify potential biases in the peer review process, such as reviewer bias or gender bias.
  4. Reproducibility: AI can be used to help increase the reproducibility of research by automating some aspects of the research process. For example, AI can be used to automatically generate code or scripts, making it easier for other researchers to replicate the analysis. AI can also be used to identify potential errors or inconsistencies in the data or analysis, improving the quality and reliability of the research.

Of course, it is important to note that AI is not a panacea for all the challenges of open science, and there are still many issues that require careful consideration and human input. However, AI can potentially help make open science practices more efficient, effective, and accessible, and there is a growing interest in exploring the use of AI in this context.

Nate: Do you think AI poses a threat to science, in particular principles of open science?

ChatGPT: While there are concerns about the potential risks and challenges associated with the use of AI in science, I do not believe that AI itself poses a direct threat to the principles of open science. Rather, it is the way in which AI is developed, deployed, and used that can potentially pose challenges or risks to open science practices.

One concern is that AI can potentially exacerbate existing inequalities in scientific research. For example, if AI tools are developed and trained on biased data or by biased algorithms, they may perpetuate or amplify existing biases in research. This could have a negative impact on the principle of equity, which is a core component of open science.

Another concern is that AI could be used to automate aspects of research that require human judgment and interpretation, potentially leading to a loss of creativity, intuition, and critical thinking in science. This could impact the principle of transparency, as researchers may rely on automated tools rather than engaging in open and collaborative inquiry.

Additionally, there are concerns about the potential misuse of AI in science, such as the use of AI to generate fake research or manipulate data. This could undermine the principle of integrity, which is a core component of open science.

Overall, I believe that AI has the potential to both enhance and challenge the principles of open science, depending on how it is developed and used. It is important for researchers and stakeholders to engage in critical discussions and careful evaluation of the ethical, social, and legal implications of using AI in science to ensure that the principles of open science are upheld and advanced.

Nate: Speaking of reproducibility. If I asked you the same questions in the future, would you give identical answers?

ChatGPT: As an AI language model, I would provide the same response to the same question every time, assuming that the context and information available to me are the same. However, it is important to note that my responses are generated by machine learning algorithms and are based on patterns and relationships learned from large datasets. While I strive to provide accurate and reliable information, my responses may not always be perfect, and they may be subject to limitations or biases in the underlying data or algorithms. Additionally, if the context or information available to me changes, my responses may also change accordingly.

Nate: Are you processing new information? In other words, still learning and evolving?

ChatGPT: Yes, as an AI language model, I am designed to learn and evolve over time as I process more information and receive updates from my creators. My responses are based on the patterns and relationships learned from large datasets and are continually refined and updated as new data becomes available. This means that my responses can evolve and change over time as I learn from new sources and refine my understanding of different topics.

Nate: I am writing a blog post in my blog ‘Crowdid’ by me, Nate Breznau. The blog is an academic blog hosted by Hypotheses.de. Would it be ok if I posted our conversation in my blog?

ChatGPT: As an AI language model, I do not have any personal or legal rights over our conversation. However, I should note that the content of our conversation is generated by machine learning algorithms and is provided for informational purposes only. While I strive to provide accurate and reliable information, my responses are based on patterns and relationships learned from large datasets and may not always be perfect or fully accurate. It is ultimately up to you to decide whether you want to share our conversation in your blog, but I would recommend that you provide appropriate context and citations to help ensure that your readers have a clear understanding of the content and its source.

Nate: Thank you, this has been a very enlightening conversation. Could you provide me with some more information about you, ChatGPT, and how my blog readers could understand how you learned to become an AI?

ChatGPT: I’m glad to hear that you found our conversation enlightening! As an AI language model, I was created by a team of researchers at OpenAI, a research organization focused on developing advanced AI technologies for the betterment of society. My development was guided by a large corpus of text data, which was used to train deep neural network models to generate human-like responses to natural language input.

The training process involved exposing the models to large amounts of text data and using sophisticated algorithms to learn patterns and relationships in the data. Over time, the models became better at understanding natural language and generating coherent, human-like responses to text input.

It’s worth noting that my development was a collaborative effort that involved a diverse team of researchers, engineers, and linguists, as well as large amounts of computing power and resources. While I can generate responses to a wide range of questions and topics, my responses are generated based on patterns learned from the data and do not represent a true understanding or consciousness of the topics being discussed.

If you would like to provide your readers with more information about how I was developed and how I work, I would recommend checking out the OpenAI website, which has a wealth of resources and information on AI and machine learning.

Love connection

I was recently asked to give a speech at a wedding. I had several requests for a copy of that speech. I provide that here. The speech is written so that those getting married remain anonymous, I will refer to them as A and B. It is also my best effort to reconstruct it, as it was not written down. It likely varies slightly from the original except for the quotes. The speech started with some personal comments and a joke or two and then it transitioned into this:

Families can be complicated. As the Dalai Lama pointed out:

If you think you are enlightened, go spend a week with your family.

Families aside, we live in a world that sometimes feels cruel, cold and in conflict. That’s why it is so powerful when two people come together and build something. A and B are building something. A sanctuary. A place where they can be safe, where they can express their true feelings, where they can expose their true selves.

What am I saying?…. I’m saying A and B are in love!

Now I don’t actually know what that is, but Dr. Seuss described it once as:

You know you are in love when you can’t fall asleep because reality is finally better than your dreams.

In another book that I admit I haven’t read much of, but is quite popular, we have John telling us that,

God is love.

The point, I think, is that love is not just a state of being. It is a choice, it is an action, it is a power that exists as a synergy. Something greater than the sum of the people who make up its parts. But at a basic level, love is commitment. Commitment is a choice. A choice to honor the idea of love.

Because a relationship will not always be pink fluffy dinosaurs, rainbows and moonbeams. Its like a river. Sometimes surging after rain fall, other times flowing mildly and still other times dried up in drought. Therefore, true love is the commitment to work through the drought. To admit that those loving feelings are not there, but to honor and cherish the partner anyways. To have faith that it will return.

Its maybe like one of my favorite authors, bell hooks, describes it:

Love is an act of will, both an intention and an action…the will to extend one’s self for the purpose of nurturing one’s own or another’s spiritual growth.

Therefore, it seems like what A and B have experienced in their 8 years together, and what I’ve experienced in my 13 years with my wonderful partner, is the transformative power of love – as in the commitment to nurture love through thick and thin.

Eleanor Roosevelt said:

It takes courage to love, but pain through love is the purifying fire which those who love generously know

A and B sit here with confidence because they have experienced this purifying fire, and are ready to experience it again and again. I see and honor their commitment tonight.

I see them create what is described by the Rabbi Jonathan Sacks:

For life to have personal meaning there must be people who matter to us, and for whom we matter, unconditionally and non-substitutably.

And their efforts are not just for them. This act of commitment to love is also a commitment to contribute something to the world that is good. That makes it just that much better for the rest of us. That nurtures humanity. As the Rabbi Amanda Greene pointed out:

We must build this world from love.

All of that! All through a commitment to love. All through love as a commitment. A commitment to love unconditionally, to fall short of this perfect ideal over and over, and to keep trying.

For this beautiful act I salute you and I love you both.

Meta-reproducibility crisis: Software edition

Its not just a ‘replication crisis‘, its a reproducibility crisis. Forget about falsification if one can’t even reproduce the workflow of another. Efforts are underway to improve transparency which allows reproduction, good. But what if even with the original data, researchers cannot reproduce numerical results simply because of where they are in time and space? What if different statistical programming languages or different packages and versions of these languages actually lead to different findings? Based on results of the Crowdsourced Replication Initiative we demonstrated that even using the same data and models, only 80% of independent replicator teams could reproduce the numerical results, even when they had access to the original Stata code.

Based on my experiences in this area, I cannot but help have a strong hunch that software or package versions threaten the computational reproducibility of our research. The fact that Stata rounds up at .5 and R rounds down already suggests that the same models might produce different results across software simply from rounding variations. Recently, I found another reason to believe this hunch.

I am a participant in SCORE, a massive collaboration to systematically investigate the reproducibility of social science research – led by the Center for Open Science. I agreed to attempt a computational reproduction of the study “Age, Inequality, and Reactions to Marketization in Post-Communist Central and Eastern Europe” by Horvat and Evans (2011).

Despite some potential differences in the data I received from the original authors and those reported in their tables, I was able to produce similar results. Similar but not exactly close. The particular coefficient I was interested in came out at -0.39 in my ordered probit (cumulative link) model, whereas their original was -0.22. As a logistic coefficient or (translated into a percentage probability), this is potentially a large difference. I used R, but was not satisfied with my results. The original authors most likely used Stata, as their public data were a .dta file – Stata’s native file storage format. Also, lets be honest, no social scientist was using R before 2010 when they likely did their analyses. My curiosity and suspicion led me to run the model in Stata. Here I got a -0.21 coefficient. Almost identical to their original study, and not surprising given that there was minor variation in case numbers they reported and in the public data file. I was still not convinced that these coefficients were actually different because the models were quite complex. So I plotted the predicted probabilities to be sure that this was not statistical artifact of some model components (intercept cut-points for example).

But my hunch was supported. These same models run in R and Stata, led to different predicted probabilities. The figure below shows that among many former Communist Eastern and Central-Eastern European countries those who are aged 60+ were less optimistic about their living standards over the next 5 years. One of their critical findings was that this gap increased form 1993 to 2007, in particular because the 60+ group became even less optimistic; although in fairness this is difficult to conclude because of all the other variables in the model. What is clear, is that they were 0.45 points apart in 1993 and 0.65 apart in 2007 (see Table 5 in their original study). When I plot the predicted values I find that these results are similar but not identical in terms of the 1993 to 2007 change, but that the probabilities are quite different across software.

I used Stata v15 and the ‘oprobit package, and I used R’s ‘MASS’ package and the ‘polr‘ function. Despite identical data (and case numbers!) the ‘polr’ routine predicts the probability of respondents answering that their standard of living will fall or fall a great deal as much higher than that of Stata. Although the relative change between age groups is similar – with a slightly steeper negative slope in R – the lines are pretty far apart, and even further apart for the age group 60+. Without unpacking each package and the exact estimation strategy taking place therein, I cannot as of yet say why. My own statistical and software abilities are by no means exceptional, but certainly above the average social scientist. Thus, it would be totally unrealistic to expect any social scientist to understand the entire routine taking place within the polr or oprobit packages. If they did, they wouldn’t need the packages and could just write their own cumulative link routine!

The implications are that using different software leads to a lack of reproducibility. As if we did not have enough to worry about in the reproducibility area already.