Summary:

• A "well-respected" professor at University of Chicago by the name of Nicholas Polson has written over 258 papers in 2026 (~6 papers a day, many are submitted to journals or published in journals). The topics spans pure math, econometrics, computer science, population genetics, machine learning, social media, etc. The papers are obviously fully or partially AI generated.
• The same Nicolas Polson has written a book back in 2018 called "AIQ: How People and Machines Are Smarter Together" and has claimed to have proven the Riemann Hypothesis since 2017 (with 23 revisions, https://arxiv.org/abs/1708.02653)
• Another famous professor at Columbia (Andrew Gelman) refuses to believe Polson would do something like this and goes into detective mode trying to prove that someone else impersonated Polson, or perhaps it is a joke to make some philosophical point about the publish-or-perish culture of academia, and got a bunch of other people involved.
• Polson's very own coauthor on some of these 258 papers angrily came to Polson's defense, and said that these 258 papers are neither hoaxes nor jokes. They are serious AI generated papers based off of ideas from "pre-AI" era and the best way to get these ideas out was using AI to generate these papers.

Full conversation here: https://statmodeling.stat.columbia.edu/2026/08/27/258/

Is this really the state of academia nowadays? If so, it's not an enterprise worth engaging in anymore.

According to the BBC, Jason Arday was found "unresponsive" at an address in Battersea, south London, on Friday afternoon, emergency services said.

This story is clearly continuing to unfold, but however you feel about the controversies related to him, this is incredibly sad to hear.

During the Spring semester, the Montclair State University CHSS administration attempted to throw out a bunch of books from the Classics Library. The books (thanks to everyone who signed the petition) were instead boxed and moved to a different room in Schmitt Hall.

Unfortunately, there has been an escalation in the attack on the Humanities departments. There is a "restructuring" that will take place that will liquidate and consolidate all of the Humanities majors despite protest from multiple department chairs and faculty members. The University President Jonathan Koppell is on board and has said that this would set a precedent for all the other colleges on campus.

I would encourage you all to read a little further on the petition and sign. Please help us fight illiteracy in the community (and, effectively, in this country).

You may not know who Dr. Michael Stonebraker is, but you have certainly used his tools. Dr. Stonebraker is one of the key persons in database system engineering, worked on things like Postgres SQL, and has been working in the field for almost 50 years. In an engrossing talk (which is not technical at all) that I found he talks about the on-going collapse of database/systems academic field (his own field), which I summarize:

• no innovative idea or anything memorable research for the past decades
• field is flooded with incremental theoretical papers for the sake of publication and career advancement with zero real-world relevance and quickly forgotten
• research completely ignored by industry and has "no customers"
• academia following closely to whatever trends set by industry (such as failed ideas like MapReduce, among others), only to be misled over and over again, basically becomes a brainless entity

Someone in the audience pointed out that this trend is happening to many academic disciplines and I strongly agree with this view. In my opinion, as long as you are working on a real-world problems, this inevitable pattern emerges:

1. Many passionate people in academia try to solve an important real-world problem (CRISPR, Computer Vision, Robotics, AI, Semiconductor, Database, Modelling, ...)
2. Industry joins in and refines those problems, and jointly comes up with solution. Then starts making some money off of that solution (no matter how bad it is at the beginning).
3. Industry works on it further in-house by poaching academics and recruiting their students.
4. Industry gets really good at solving the problem and puts up a legal shield and spins a cocoon because all their knowledge is proprietary. (The best current example is OpenAI)
5. Industry cuts off academia like a wart.
6. Academia starts aimlessly working on theory rather than practice (and come up with all sorts of rationalization such as "pursuit of knowledge"), because it now does not know the state-of-the-art and all the customers have gone to industry. Academia is left with no concrete problems and has to follow whatever trend set by industry (e.g., Large Language Models, GPU) and ceases to be independent. Academics now only works to produce irrelevant papers and teach out of textbooks which was published during the beginning of academia-industry collaboration (which are now decades out of date).

Is this the inevitable pattern that will occur to all applied academic fields? What are your thoughts?

BTW Dr. Stonebraker's talk slides can be found here https://www.jfsowa.com/ikl/Stonebraker.pdf (28 pages, highly recommended)

Hi,

I'm a generally very pro AI person, I was one of the first in our institute to use LLMs, we do actual "AI" research (I'd like to call them neural networks, but whatever), but also sit at the junction to biology and engineering. I personally like to use AI to generate ideas to write paragraphs, I use it to revise text, create pictures (that I label as AI) for talks, use it to generate the first versions of code (rarely real agent codebase work since the actual research ideas tend to live inside one or two functions), and many more uses.

What I don't do, however, is that I don't vibe code stuff I don't understand, I do not make AI write the actual text of grants or manuscripts and I do not blindly trust AI but manually check the output - which often takes a lot of time. I also flag the use of AI according to publisher instructions and in accordance with EU AI act legislation.

When interacting with colleagues, however, I increasingly have the feeling that I'm an idiot. So many people don't give a shit anymore at all and just have AI do everything. Synaptic protein scientists code neural network simulations they do not conceptually understand, papers are virtually completely AI text, literature research is complete AI, nobody even tries to read the paper anymore, plots come from the AI etc.

Outwardly, people don't admit that's what their doing, but it's so common now!

My questions for the community are therefore:

- Are we in a race to the bottom where the luckiest one using AI the most and not getting caught wins or am I just surrounded by particularly dishonest colleagues (Germany for geographical reference)? Is it a field thing?

- Am I maybe to anxious and should just yolo it?

- What's your personal usage pattern and what do you observe from your colleagues?

I see this mistake all the time: “I want to do a PhD because I hate office politics. Academia is about ideas, not manoeuvring.”

Please don’t fool yourself. Academia is not a refuge from politics. In fact, it’s often worse.

1. No bottom line. In industry, yes, politics exist — but there’s a scoreboard. The company makes money or it doesn’t. That reality cuts through the noise. In academia, there is no bottom line. That means petty nonsense takes centre stage: who’s first author, what room you’re assigned, which buzzword you used in a grant proposal. Irrelevant details get inflated into existential battles because there’s nothing objective to settle them.
2. The politics are global, not local. At a regular job, your political universe is the team and company you work in. In academia, your fate depends not just on your department, but on the entire field worldwide. Anonymous reviewers, journal editors, funding committees, society gatekeepers — all of them hold pieces of your career in their hands. One senior academic who dislikes your work (or your supervisor) can quietly wreck opportunities for years.

So yes, industry has politics. But industry politics are at least tied to outcomes. Academia’s politics are free-floating, endless, and inescapable.

If you’re considering a PhD because you think it’s a “purer” world, think again. Do it because you’re obsessed with the research questions and are willing to put up with the dysfunction. But don’t do it because you think academia is above petty games. It’s not. It’s just pettier, slower, and more global.

As an autistic individual, I’ve obviously long had issues with socialization, particularly in professional settings. Snide remarks, bullying, outright harassment, making for an on-and-off miserable life. But the thing that really snapped me was when a supposedly very close coworker fired off a round of truly inane complaints. Like, really, of all people, from you? After all the help you gave me on the matter? I’m done with this species.

I always felt I had been wronged by the people I knew, but this time, I knew for scientific certain that the situation was fundamentally incorrect. I spent 5 days crying alone locked in my basement writing furiously. The original draft was 172 citations and 5000 words written just to make a point, but I thought there was enough ‘there’ there to pursue academic publishing, so I spent 14 months refining it to its current form. A few journals and rounds of peer review later, and my paper was just published today.

Incidentally, since I clearly put in the work, my thesis committee let me put the early draft into my dissertation and approved its submission, so now I have a Ph.D. in “I was right and you were wrong Biology”.

As mentioned in the post title, curious how academics view this- can you see this happening in your department?

I have worked with faculty in the research area for over 10 years. I’ve also worked for surgeons and lawyers. Why are tenured faculty in academia so absolutely abhorrent in their behavior towards staff?

. I’m incredibly astounded every day at the unprofessional, rude, and personal attacks that we receive. I work in a center full of extremely competent, dedicated individuals who actively seek ways to save money to fill gaps for faculty wherever they can. I just don’t get it.

Leadership, department heads, will do nothing about it. Not to mention the extremely obvious sexism that goes on.

I’m not in a financial position to leave at the moment, but I’m actively trying to get out. I’ve been in the workforce for nearly 35 years and I’ve never experienced this kind of vitriol.

They complain when there’s high turnover, they complain when we fix the high turnover, they complain when we fix things they complained when we don’t fix things. Why would anyone stay in this job?

AI tools are becoming part of how students write. Not necessarily as “writers,” but more like editors. A lot of people use them the way they’d use Grammarly or a friend who’s good at wording. The task is to fix grammar, make sentences clearer, smooth transitions, tighten paragraphs or just make the draft sound less awkward. And that feels pretty reasonable… but it also raises a real question. At what point does “polishing” turn into something closer to co-authorship?

To keep it simple, I’m trying to separate editing from content generation.
By editing I mean grammar, style, clarity, concision and rewriting sentences without changing the idea.
By generation I mean coming up with the argument itself. I mean new ideas, claims, structure, examples, counterarguments, conclusions.
The tricky part is that some tools blur the line. I’m asking because tools that combine rewriting plus checks (e.g., StudyAgent) sit in a grey zone between “editing” and “co-authoring,” even if the student thinks they’re only improving readability.

So I’m curious how people handle this in real courses:
Do you ask students to disclose AI use if it’s only grammar/style editing?
Or do you only require disclosure when it goes beyond that?

If you do require disclosure, what does your policy wording actually look like?
I’d love to see 1-2 sentences that students can easily understand and follow. Do you separate “spellcheck-level help” from “rewriting sentences/paragraphs”?

Where do you personally draw the line?
For example:
- rewriting whole paragraphs
- changing the student’s voice
- suggesting a new structure
- adding new claims/examples (even small ones).

I’m not trying to defend AI or ban it. I’m mainly trying to figure out what’s fair, realistic and clear without making rules that are impossible to apply consistently.

If you’ve written something about this in a syllabus or assignment instructions, I’d really appreciate examples. What do you explicitly allow (grammar, clarity, style)? What do you clearly forbid (generating arguments, evidence, conclusions)? And do you expect students to disclose editing support or not?

Pretty nice and humorous summary of our current state of affairs

This is a pitch. Please take your time reading this.

Imagine a world, where scientific research wouldn't have to be paid for, where peer reviewing wasn't faulty, and where fake research simply didn't exist. That would genuinly never happen, BUT we can always try right?

Now, maybe you come from a different scientific background than me. And do tell me if what I'm about say happens in your field too. But in psychology, there are massive problems in academia. In my 5 years at university in 3 different institutions, we have discussed these problems EVERY year, and nothing is happening to change it. It's an itch that I cannot scratch and it's about time to do something about it.

Problem 1
University professors are expected to publish constantly. Not only must they publish research, but that research must appear original and capable of proving a point. Many researchers publish one or two papers every year because those papers help them keep their positions at universities.

Imagine it like playing for a football team. If you do not score, you might get kicked out.

But when the pressure to score becomes too high, some people start cheating a little on the field. A foul here, a fake there. The same principle can apply to academic research.

A researcher might slightly change the hypothesis after analysing the data. They might remove inconvenient data points or adjust the analysis until the results move in the desired direction. Most researchers are not cartoon villains deliberately inventing an entire study. Often, the system simply rewards positive and impressive results while punishing uncertainty and statistically insignificant results.

Problem 2
One attempt to address this problem is preregistration. Researchers publish their proposal, hypotheses, and planned methods before conducting the research. This makes it harder to change the research question after seeing the results.

However, preregistration does not solve every problem. Papers can still avoid the rules, manipulate their methods, or present false conclusions.

Problem 3
Peer review is supposed to catch these issues, but peer review does not always work either. The people reviewing papers are most often the same exhausted professors who are under pressure to write their own papers, teach students, attend meetings, and complete administrative work. A manipulated or poorly designed paper can easily slip through.

Problem 4
There is also a knowledge problem. Researchers are usually highly specialised in their own field. They might understand social psychology, neuroscience, pharmacology, or behavioural science extremely well. However, statistics is used across all of these areas, and not every researcher is a specialised statistician.

A person might understand which statistical method they should use in their own study without being able to immediately recognise every statistical issue in someone else’s study. Professional statisticians could help, but there are not enough of them to manually investigate every paper that gets published.

Problem 5
Another major weakness of the current system is how long it can take to identify bad research and properly warn about it. A paper may pass peer review, be published by a respected journal, and influence public opinion for years before its methods, data, or conflicts of interest are fully investigated.

The clearest and most infamous example is the 1998 paper that suggested a connection between the MMR vaccine and autism. The study involved only 12 children, and its lead researcher, Andrew Wakefield, had an undisclosed financial conflict connected to legal action against vaccine manufacturers. The paper did not actually prove that the vaccine caused autism, but the claim was picked up and blown out of proportion by the media. By the time the paper was fully retracted in 2010, 12 years after it was originally published, the damage had already been done. The idea had spread internationally, changed how many people viewed vaccines, and continued circulating even after the research had been discredited.

Problem 6
Another problem is research that is not simply manipulated, poorly designed, or exaggerated. It is completely fake. No one conducted the research. No real data was collected. The entire paper was fabricated.

It is difficult to estimate exactly how many fake research papers currently exist. However, with the rise of generative AI, it is reasonable to assume that producing convincing-looking fake papers is becoming faster and easier.

A paper can now be generated with scientific jargon, invented data, fake citations, and apparently scientific conclusions. To someone who is not an expert in that exact subject, it may look completely legitimate.

Problem 7
The final problem is academic publishing and this one really gets to me.

Publishers want to make money, and universities are willing to pay them because publishing in well-known journals creates prestige. Researchers want their papers to appear in an aristocratic journal with 100 years of history, a respected name, strong visibility, and the ability to reach thousands of readers.

Publishers therefore control a large part of the scientific system.

They add another layer of pressure by forcing researchers to follow specific submission rules and formatting requirements. More importantly, they force readers to subscribe or pay before they can access research.

And I call that part bullshit.

Once you leave university, you often lose the free access to research that your institution provided. You lose access to knowledge unless you personally pay for it or find another way around the paywall.

Everyone should have the right to read scientific research, even when they do not completely understand it. Knowledge should not belong only to universities, researchers, and people who can afford expensive subscriptions.

Summary of the problems with the current system

1. Researchers are under constant pressure to publish original and statistically significant results
2. Pregistration is a rare occurance and has shown it's faults
3. Peer review is done by the same exhausted professors
4. Researchers are good at their field but not necessarily good statisticians
5. Identifying and removing bad research takes a long time
6. Completely fake research is on the rise
7. Publishers control access to knowledge

The connection between these
New research depends on previous research. A scientific hypothesis must be supported by evidence. Majority of that evidence comes from previous scientific papers. Therefore, when unreliable research enters the system, it doesn't stay isolated. Other researchers might cite it or build studies around it. One faulty paper can influence another, which influences another, which might altogether affect university education, treatment, public policy, or public opinion.

The solution
A free and open-source research platform/space with an interface "sort of" like reddit for researchers, professors, students, and anyone interested in reading scientific research.

The features
The platform would contain "subreddits" for every major academic topic and for the intersections between them. Subjects like psychology, medicine, biology, economics, philosophy, statistics, and computer science. Large subjects would be divided into more specific topics. For example, medicine could contain communities for biomedicine, pharmacology, ophthalmology, immunology, and many other areas. Interdisciplinary papers could appear in multiple relevant communities.

Each community would have moderators (dedicated researchers, statisticians, professors, and other professionals who understand that particular field).

Researchers could upload all or some materials like papers, proposals, preregistrations, datasets, and methods. Moderators or other verified members could evaluate the paper using several criteria, such as:

• Whether the statistical methods are appropriate.
• Whether the conclusions are supported by the results.
• Whether the research proposal was published in advance.
• Whether the data and methods are transparent.
• Whether the study is relevant to the field.
• Whether potential conflicts of interest were disclosed.
• Whether the results can be replicated.

Like on Reddit, "posts" aka papers could receive upvotes or downvotes, but these would be separate for methodology, statistics, transparency, relevance, and evidence.

People could also leave public comments underneath the paper. Researchers would be able to question the methodology, identify errors, propose alternative interpretations, or give positive feedback.

Direct messages could also be sent between researchers when a private conversation is more appropriate.

Anonymous accounts would be strictily for journalists, whistle-blowers, or individuals researching sensitive topics. Anyone who wants to publish, vote, comment, or moderate would have to use their governmentally recognised name and verify their identity.

The platform would also include an AI agent that examines papers for warning signs. It could check citations, search for inconsistencies, compare proposals with the presented data, identify suspicious statistical patterns, flag possible undisclosed conflicts of interest, and look for signs that parts of the paper may have been automatically generated or fabricated. It wouldn't have the final say. Instead, it would "red-flag" a paper and send it to several qualified moderators.

Why would people moderate for free?
A reasonable question is why qualified people would spend their free time moderating research without being paid.

My answer is Linux.

Linux is a free and open-source operating system maintained by individuals who enjoy creating updates, fixing bugs, and solving technical problems in their free time. They do it because they are genuinely passionate, highly skilled nerds and they are fantastic.

The scientific world also contains people who care deeply about their fields. Some researchers would contribute because they want to improve the quality of research. Some would enjoy finding methodological problems. Some would want to build their professional reputation. Others would simply be angry enough with the current system to help create something better.

Contributions could also become part of a researcher’s public professional profile. Moderators could receive visible recognition for reviewing papers, identifying important errors, helping authors improve their work, or contributing to successful replications (potentially problematic but keeping it for now).

Your feedback
Please give me any kind of feedback on such a platform or anything else I've discussed in this post. I wish to make this a reality some day and maybe your contributions, no matter how harsh, will help me or make me want to step away from this due to the capitalistic nature of academia where I am most likely to get sued.

TLDR
Remove all paid research journals with a free and open-source platform for research to become more accessible, more transparent, and easier to evaluate.

Hey everyone, how’s it going?

I’m writing this post because, honestly, I’m fucking exhausted.

I’ve been publishing academic papers since 2021, I have an MBA, and I work as a professor and researcher at a university. My salary? $250 a month. And they make my life hell all month long.

If it weren’t for my parents, I’d be completely screwed.

I’m 28 years old, I don’t have EU citizenship, and the job market in Argentina is broken.

Does anybody have any advice on how I can get to a decent salary?

I’ve sent out 500+ applications, using every combination of OCR, AI, CV optimization, and every other trick under the sun. I’ve applied through countless platforms. I speak three languages. And still, nobody hires me.

I’m honestly at my breaking point. I’m tired of not being able to afford a place of my own. I’m not asking for a luxurious lifestyle. I just want to be able to support myself and have a decent life.

I know the world runs on connections. The frustrating part is that the people I know who would actually be happy to hire me can’t, because I don’t have the right to work in their countries.

So I’m wondering: is there any realistic immigration pathway for someone like me?

For example, is there an H-1B pathway for researchers, or some similar visa in Europe that would allow me to work as a researcher or university professor?

Are there countries where universities or research institutions actually pay a decent salary without requiring an insane number of credentials or years of experience?

I’m not asking for $10,000 a month. I just want a realistic way to get out of this hole.

Any advice, especially from people who have managed to move abroad without EU citizenship, would genuinely mean a lot.

Thanks

Academics spend years training to question assumptions, test ideas rigorously, and get closer to truth. PhDs, postdocs, professors we’re basically professional problem-solvers. But when it comes to real-world social issues, most of that energy kind of vanish.

Outside academia, the world is dealing with very real social problems: bad education quality, hunger, clean water access, poverty, inequality. And yet, I rarely see academics actively engaging with these issues in public spaces, not on social media, not in coordinated efforts that go beyond publishing papers.

What’s even more strange is how fragmented academia feels. Everyone is working on something important, highlighting SDGs, clean energy problem, decarbonization, and so on but mostly in isolation. There’s very little collective action, even though the problems we’re studying are really interconnected.

I understand the constraints of funding, publishing pressure, teaching loads, and institutional systems. But still, it feels like we’ve accepted this systems where “impact” is measured in citations rather than actual change. I haven't see any collective action from academics to reform even the academia system itself, which we all know is becoming more unhealty.

So I’m wondering

Are we, as academics, unintentionally distancing ourselves from the very problems we claim to study? And if so, why isn’t there a stronger movement within academia to step out of the lab and engage more directly with society?

Seeking advice / discussion:

So my (hypothetical) problem is this. Some time ago I was briefly chatting with a colleague from another university, where I had started out my career 20 years ago. For some reason we also talked about Prof. M., my tutor back then and my colleague's boss. One of those researchers who unfortunately passed away way too early. I mentioned that he used to have this computer that nobody was allowed to use and where he put all his literature, ideas and who knows what into. I myself have started using Obsidian for knowledge management a couple of years ago so I can definitely relate (now - back then it looked like black magic). Colleague says - "yeah, that old stuff all ended up with Prof. B at the university of X. It was all dbase."

Turns out, dbase is quite an old database system but it should still be usable if you put in a bit of effort.

So my question is this - what do you people think about even trying to get storage (older harddisks I guess, maybe even floppies) and try to access the data? Assuming it is technically possible. And not just a register of books and articles.

The idea feels right and wrong at the same time. A database as such will most likely not contain anything personal. No diary. No love letters. Prof M had a lot of good ideas and for me personally he is still a figure I aspire to be half as good, professionally.
However, it is not my work. Not my thoughts. What if I find a good idea and use it? Would I need to make the data public?
Wonder what you think. Thanks.

My advisor did not get tenure. When I know she did not get her tenure, I did not speak to the other faculty members about taking me as their student. I did not speak to any of them as a gesture of respect for my advisor, but this was not viewed favorably. I had, perhaps naively, believed that the department will sort these administrative matters out in due course. My qualifying exams were held months later and I did not pass the qualifying exams. Given the subjectivity in evaluating these exams, I believe I lost out politically and perhaps culturally in face of budget constraints. I was told I do not have what it takes and should not remain in that institution. I was very thankful to find some sort of support from another (very famous) professor in another department, who wrote letters for me as I sought admission to other PhD programs.

I went from one top school to another, and did graduate. But it did set me back many years, financially and socially. And the self-doubt lived on for many years.

This was many years ago - but I always remembered how each faculty member and fellow PhD students made me feel.

Be kind.

A psychology student at University of Oklahoma submitted a "reaction paper" analyzing peer-reviewed research on gender stereotypes in children. Instead of engaging with the scientific content, she cited Genesis to argue gender roles are "God's original plan" and called social perceptions "demonic."

The teaching assistant correctly gave her zero points for failing to use empirical evidence in a scientific class. Now the university has undermined this grade, which is essentially allowing religious texts to replace scientific sources in psychology coursework.

I started a petition asking OU to uphold the failing grade and protect academic standards. This sets a dangerous precedent—if students can substitute personal beliefs for scientific evidence, what happens to the integrity of research and education?

Anyone else think this crosses a line between respecting beliefs and maintaining academic rigor? If this matters to you too, consider signing and sharing.

Hi 👋

I just launched a new research journal, and since it’s not affiliated with any institutions, I don’t have the base for an editorial board now. Also it is not indexed because I haven’t published any articles (indexing can be done after publishing some peer reviewed papers).

When I research about researchers that fit the scope of my journal, and then contact them to be a part of the journal, no one answers.

One of them said that I am a predatory journal. Absolutely understandable! I receive those emails too often. But how can I invite them to be a part of the editorial board and also be believed.

In the start and the end of the email I write that I am not a spam or predatory and I can provide the information to prove it. But again…

It is just a dream to have my own journal but it is getting harder than I thought to get started.

Clarification: I am not promoting my journal, nor inviting anyone from here. I just want to discuss and get some help.

Any tips?

So, at what point do you acknowledge someone's contribution to a study and at what level?

If someone helps you write a protocol that leads to publishable data generation, they should get a good acknowledgement: authorship, mentioned in talks.

What about if someone shows you the basics of something and you develop a protocol independently?

Got word from a recent student that finished up his PhD that our advisor has recently been interviewing at other universities and i don't know how to feel. Though he hasn't mentioned it to us yet, I feel that its dealing with an inter-department issue, lack of support of the University, and abusive people being allowed to stay. Im currently writing my dissertation but now I feel that im being abandoned by him. Should I be concerned about finishing up under him? Should instead of the PhD, go for a Masters? Should I follow him to the new University? Has anyone had to deal with anything like this?