AI Slop Worsens the Degenerative Spiral of Biology Research

 My long 2021 post "'The Degenerative Spiral of 'Grand Explanation' Academia" was one that painted a troubling portrait of malfunction and deceit in the world of academic researchers purporting to have grand explanations for great mysteries of nature. Has there been improvement since that time? To the contrary, things seems to be getting worse. As Exhibit A to back up the claim that the generative spiral of "grand explanation" academia is worsening, I may offer a recent article by Ross Andersen in The Atlantic, one entitled, "Science Is Drowning in AI Slop." We read this, referring to the "large-language models" used by so-called artificial intelligence or AI:

"Almost immediately after large language models went mainstream, manuscripts started pouring into [scientific] journal inboxes in unprecedented numbers. Some portion of this effect can be chalked up to AI’s ability to juice productivity, especially among non-English-speaking scientists who need help presenting their research. But ChatGPT and its ilk are also being used to give fraudulent or shoddy work a new veneer of plausibility, according to Mandy Hill, the managing director of academic publishing at Cambridge University Press & Assessment. That makes the task of sorting wheat from chaff much more time-consuming for editors and referees, and also more technically difficult."

We read about companies called paper mills that help scientists produce papers, either by generating from scratch a fake paper, or by generating dubious or fake paragraphs or dubious or fake images that a scientist can use in his paper. AI makes it much easier for such science fakery to occur. These paper mill companies don't have advertisements with phrases such as "We'll Help You Fake Things!" Instead, they claim to offer "editorial services" or maybe "creative consulting" or "literary facilitation" or some other euphemism.

It seems that many scientists use such "paper mills" to speed up the job of producing a scientific paper, in a shady way similar to a football player taking banned steroids to boost his performance. But with all the AI tools out there, such as ChatGPT, it seems that a scientist does not even have to become involved with external paper mills. Similar results can be achieved from a scientist's desktop, when he uses some AI tool such as ChatGPT. 

We read that AI tools are also being used to write peer reviews. The article says, "Pangram Labs recently analyzed thousands of peer reviews that were submitted to ICLR, and found that more than half of them were written with help from an LLM, and about a fifth of them were wholly AI-generated."  We read this: "AI science slop has spread beyond the journals now, and is also overrunning other venues for disseminating research."

Preprint servers are sites such as the Cornell physics paper server and the biology preprint server Biorxiv.  Andersen states this:

"But in the months after ChatGPT was released, preprint servers experienced the same spike in submissions that journals did. Ginsparg, who is now a professor of information science at Cornell, told me he hoped that this would be a short-lived trend, but the rate of submissions continues to rise. .. A similar influx of AI-assisted submissions has hit bioRxiv and medRxiv, the preprint servers for biology and medicine. Richard Sever, the chief science and strategy officer at the nonprofit organization that runs them, told me that in 2024 and 2025, he saw examples of researchers who had never once submitted a paper sending in 50 in a year."

Andersen ends on this gloomy note, describing the most severe degenerative spiral of the scientific literature:

"When I called A. J. Boston, a professor at Murray State University who has written about this issue, he asked me if I’d heard of the dead-internet conspiracy theory. Its adherents believe that on social media and in other online spaces, only a few real people create posts, comments, and images. The rest are generated and amplified by competing networks of bots. Boston said that in the worst-case scenario, the scientific literature might come to look something like that. AIs would write most papers, and review most of them, too. This empty back-and-forth would be used to train newer AI models. Fraudulent images and phantom citations would embed themselves deeper and deeper in our systems of knowledge. They’d become a permanent epistemological pollution that could never be filtered out." 

In 2016 Scientists Confessed Science Research Was Broken, and in 2026 Things Are No Better

 A 2016 story article in Vox.com was a remarkable confession of how bad things are in the world of scientific research. The article was entitled "The 7 biggest problems facing science, according to 270 scientists."  We read this:

"We heard back from 270 scientists all over the world, including graduate students, senior professors, laboratory heads, and Fields Medalists. They told us that, in a variety of ways, their careers are being hijacked by perverse incentives. The result is bad science....Today, scientists’ success often isn’t measured by the quality of their questions or the rigor of their methods. It’s instead measured by how much grant money they win, the number of studies they publish, and how they spin their findings to appeal to the public."

The author writes about what is called publication bias, the tendency of science journals to prefer publishing studies that report finding some positive effect, rather than studies that fail to report such an effect, finding what is called a null result.  We read this: 

"Scientists often learn more from studies that fail. But failed studies can mean career death. So instead, they’re incentivized to generate positive results they can publish. And the phrase 'publish or perish'  hangs over nearly every decision. It’s a nagging whisper, like a Jedi’s path to the dark side."

The statement won't be very clear to the average reader. What the author means by "studies that fail" is not studies that fail to follow good science practice or that fail to be completed, but instead studies that fail to report a positive result. An example would be a study testing for whether removing one particular gene from mice has an effect on their memory, and which reports no effect from such a removal. By "failed studies can mean career death" the author means studies that failed to report a positive effect and that then did not get published, because of publication bias in which positive results are preferred.  A scientist doing enough of such studies that were not published might end up with a low count of published papers. 

We read about conflicts of interest:

"Already, much of nutrition science, for instance, is funded by the food industry — an inherent conflict of interest. And the vast majority of drug clinical trials are funded by drugmakers. Studies have found that private industry–funded research tends to yield conclusions that are more favorable to the sponsors."

Such conflicts of interest taint neuroscience research, because a large fraction of neuroscience research is funded (directly or indirectly) by pharmaceutical companies and biotech device manufacturers hoping to produce some result they can claim is scientific evidence in favor of some pill or device they are selling.  We read in the article that some professors are spending up to 50% of their time writing research grant proposals. 

We get this quote:

"'As it stands, too much of the research funding is going to too few of the researchers,' writes Gordon Pennycook, a PhD candidate in cognitive psychology at the University of Waterloo. 'This creates a culture that rewards fast, sexy (and probably wrong) results.' ”

A culture that rewards wrong results? How messed up is that?

We read the following:

"The problem here is that truly groundbreaking findings simply don’t occur very often, which means scientists face pressure to game their studies so they turn out to be a little more 'revolutionary.'  (Caveat: Many of the respondents who focused on this particular issue hailed from the biomedical and social sciences.)

Some of this bias can creep into decisions that are made early on: choosing whether or not to randomize participants, including a control group for comparison, or controlling for certain confounding factors but not others....Many of our survey respondents noted that perverse incentives can also push scientists to cut corners in how they analyze their data.

'I have incredible amounts of stress that maybe once I finish analyzing the data, it will not look significant enough for me to defend,' writes Jess Kautz, a PhD student at the University of Arizona. 'And if I get back mediocre results, there’s going to be incredible pressure to present it as a good result so they can get me out the door. At this moment, with all this in my mind, it is making me wonder whether I could give an intellectually honest assessment of my own work '.”

We read a quote from a Joseph Hilgard who says, "The scientist is in charge of evaluating the hypothesis, but the scientist also desperately wants the hypothesis to be true.”  We read the claim that 85 percent of research is "routinely wasted on poorly designed and redundant studies."  We read  the claim that up to 30 percent of research turns out to be wrong or consist of exaggerated results. 

We read about how badly published results fail to be replicated. We have a big boldface section header saying this:

"Replicating results is crucial. But scientists rarely do it."

We get this example

"The stats bear this out: A 2015 study looked at 83 highly cited studies that claimed to feature effective psychiatric treatments. Only 16 had ever been successfully replicated. Another 16 were contradicted by follow-up attempts, and 11 were found to have substantially smaller effects the second time around. Meanwhile, nearly half of the studies (40) had never been subject to replication at all."

We have this statement about misleading science journalism and misleading university press releases:

"Science journalism is often full of exaggerated, conflicting, or outright misleading claims...Sometimes bad stories are peddled by university press shops....Indeed, one review in BMJ found that one-third of university press releases contained either exaggerated claims of causation (when the study itself only suggested correlation), unwarranted implications about animal studies for people, or unfounded health advice....The 'toxic dynamic' of journalists, academic press offices, and scientists enabling one another to hype research can be tough to change, and many of our respondents pointed out that there were no easy fixes — though recognition was an important first step."

The long 2016 Vox article mentions some ways that this sorry state of science research could be improved.  But in the ten years since the article was published, there has been no improvement in the sorry state of science research. All the problems discussed in the 2016 article are still there, and still exist as badly as they existed in 2016.  There is no evidence that research scientists and science journalists are improving their dysfunctional and defective methods.  The many severe problems mentioned are only part of the problems that exist.  Many other severe problems in science research and science journalism are not mentioned in the Vox article, such as these:

(1) The tendency of scientific researchers to try to do research that supports prevailing dogmas of scientists, which are often groundless dogmas or poorly supported dogmas, rather than to do objective research that takes a "follow the evidence wherever it leads" approach. 

(2) The strong economic motivations that underlie misleading clickbait headlines, motivations such as the desire to produce page views that are profitable because of revenue-generating ads on such pages. 

(3) The use of way-too-small study group sizes in fields such as neuroscience, resulting mostly in unreliable "false alarm" results. 

(4) The use of poor methods of measurement in fields such as neuroscience, such as the widespread use of unreliable judgments of "freezing behavior" to judge fear or recall in rodents, rather than other more reliable methods. 

(5) A failure to follow a detailed blinding protocol.

(6) The extensive use of "keep torturing the data until it confesses" tactics, in which scientists fail to commit themselves to one straightforward method of gathering data and analyzing data, and instead act as if they had a license to endlessly play around with data, subjecting data to the most bizarre and convoluted arbitrary analysis pathways, that end up distorting and contorting the data gathered. 

What goes on in today's experimental neuroscience

When such problems exist in abundance, neuroscientists are largely engaging in a sham and a scam when they take federal money and pretend to be engaging in rigorous experimental science. 

Research science and science journalism are broke, and there is no sign that they are slowly mending themselves. 

A recent article on the Retraction Watch site is captured in the screen shot below. Notice the graph showing that the growth of fake or shoddy "paper mill" papers is stronger than the growth of regular scientific papers. 

So-called "paper mills" are for-profit companies that offer "editorial services" to scientists, which can range from writing much of a paper to writing an entire paper. The outputs of paper mills are very often fake papers and programmatically generated papers in which everything is written by computer programs, programs that steal most of their text from other papers. Such paper mills make heavy use of AI programs such as ChatGPT, which often give wrong answers or low-quality text sometimes called "AI slop." Someone can ask a program such as ChatGPT to generate a hypothetical paper sounding like the typical paper published in some line of research. The same person might then try to get the AI-generated paper published as a real paper. Or someone might ask a system like ChatGPT to write a summary of research on some narrow topic.  That person might then try to get the paper published as a "review article" or "systematic review" or "meta-analysis." Many scientists unwilling to get involved with shady paper mills are using AI systems such as ChatGPT to write much of the text of their papers. The integrity and credibility of the paper may be compromised by the use of AI-generated text not written by any mind understanding the topic discussed. 

As Exhibit A to back up the claim that the state of scientific research may be worsening, I may offer a recent article by Ross Andersen in The Atlantic, one entitled, "Science Is Drowning in AI Slop." We read this, referring to the "large-language models" used by so-called artificial intelligence or AI:

"Almost immediately after large language models went mainstream, manuscripts started pouring into [scientific] journal inboxes in unprecedented numbers. Some portion of this effect can be chalked up to AI’s ability to juice productivity, especially among non-English-speaking scientists who need help presenting their research. But ChatGPT and its ilk are also being used to give fraudulent or shoddy work a new veneer of plausibility, according to Mandy Hill, the managing director of academic publishing at Cambridge University Press & Assessment. That makes the task of sorting wheat from chaff much more time-consuming for editors and referees, and also more technically difficult."

Because of the AI slop problem, the state of science research may be even worse today than the very bad state described in the 2016 Vox article. 

Neuroscientists Keep Peddling Explanatory Snake Oil

 In the nineteenth century a widely practiced scam was the sale of snake oil. A snake oil salesman would typically travel around from town to town, typically new towns in the western United States that had a shortage of doctors.  He would often travel in a horse-pulled wagon called a medicine wagon. The snake oil salesman would make all kinds of groundless claims about the medicinal value of his worthless bottles. After a good day's sale, the snake oil salesman would be off to the next town. By keeping on the road, he would avoid the problem of customers demanding their money back because the product failed to work. Below we see a newspaper ad placed by one of the traveling snake oil salesmen:

Notice the sweeping claim in the ad: the claim that the seller could "heal all manner of disease." Typically the person making such a claim would be selling products of little or no medicinal value. 

As the Wild, Wild West of the United States got more and more tame and adequately filled by regular doctors, snake oil treatments fell into disrepute, and the very term "snake oil" became a term synonymous with cons and cheats. But it is easy to imagine how snake oil manufacturers could have caused snake oil to become very prestigious.  All that would have been needed was the establishment of Departments of Snake Oil Medicine in colleges and universities, populated by Professors of Snake Oil Medicine. 

Once some type of claim gets taught by some type of department in colleges and universities, the public starts thinking of the claim as respectable and well-founded. What happens is that a university or college has great prestige, and is regarded as a lofty teacher of truth and a storehouse of knowledge. So when some new claim gets institutionalized by the establishment of a university department or college department, people tend to think such a claim is well-established. If there were hundreds of Departments of Astrology in colleges and universities all over the country, people would tend to think that astrology is well-established, and that consulting your horoscope is a good way to judge your future. 

We can imagine Departments of Snake Oil Medicine getting established in universities and colleges, aided by the social construction of the triumphal legend that snake oil advocates like Texas Jack were giants of medicine whose authority we should kneel to:

But how could some Department of Snake Oil Medicine ever produce research that would give some veneer or aura of scientific respectability to the claims of snake oil advocates? That would be relatively easy. A variety of bad research techniques could be employed. It would work rather like this:

(1) When snake oil advocates did studies that showed no medical effectiveness in people using snake oil, such studies would simply be filed away in the file drawers of scientists, and not submitted for publication. 

(2) Snake oil advocates would run very small studies, and by pure chance a certain number of them (maybe 5% or so) would seem to show marginal effectiveness. Such studies would be the ones submitted for publication in journals. 

(3) Noise-mining and cherry-picking could be heavily utilized. The case histories of thousands of snake oil users could be very carefully scanned, to look for cases in which some type of ailment (perhaps an infectious disease such as the flu) seemed to become less troubling at about the same time someone had drunk or applied snake oil. Such cases would be heavily promoted as proof of the wonderful effectiveness of snake oil. 

(4) Bad measurement techniques and poor analysis techniques could be used when evaluating someone's health, allowing a kind of see-what-you-are-hoping-to-see analysis. For example, when studying the effectiveness of snake oil in treating fevers, snake oil advocates might rely on dubious "rate how you feel on a scale of 1 to 10" survey answers, rather than much more reliable thermometer measures of a person's temperature. 

(5) Misleading visuals might be used, such as body maps showing in bright red regions of the body allegedly treatable by snake oil medicine. 

By the use of such techniques and many similar misleading and poor-practice techniques, Professors of Snake Oil Medicine could easily produce papers or articles that seemed to provide superficial evidence for the effectiveness of snake oil treatments, even though the treatments had no effectiveness. And if the Professors of Snake Oil Medicine were to get heavy funding (directly or indirectly) from snake oil manufacturers who made tons of money from selling snake oil, we can imagine that many university and college Departments of Snake Oil Medicine could stay well-funded. Of course, such professors would have a strong financial motive to produce results pleasing to their corporate sponsors. 

 In a previous post I stated the rule below:

The rule of well-funded and highly motivated research communities: almost any large well-funded research community eagerly desiring to prove some particular claim can be expected to  occasionally produce superficially persuasive evidence in support of such a claim, even if the claim is untrue.  

Such a rule would help Professors of Snake Oil Medicine to be able to claim that they were producing some studies in support of their claims. What would also help very much would be if the Professors of Snake Oil Medicine were to succeed in enforcing taboos and if such professors were to succeed in demonizing, slandering and gaslighting all those who presented evidence against the effectiveness of snake oil.  If such professors somehow made it a taboo to do research discrediting the claims of snake oil proponents, that would be a huge element in helping Departments of Snake Oil Medicine to become well-established.

Now, you may think that the imaginary tale I describe above is one too hard to believe. But the truth is that there actually occurred something very much like what I have described above. What happened is that they established in colleges and universities Departments of Neuroscience dedicated to the propagation of the belief that minds are produced by brains, and that brains are the storage place of human memories. Such claims were very much explanatory snake oil. Although countless billions of dollars have been spent trying to prove such claims, they have never been established by robust evidence. To the contrary, research on brains has produced innumerable reasons for rejecting the claim that minds are produced by brains, and that brains are the storage place of human memories. Such reasons are discussed in the posts of this blog site. 

So how is it that our Departments of Neuroscience have stayed in business and had such high influence? How have Professors of Neuroscience got so many people to believe the unbelievable dogmas they teach? This occurred because such professors used techniques just like I described above. Some of the techniques that such professors have used are listed below:

• Quick and dirty" experimental designs
• Way-too-small study group sizes
• Cherry-picked data subsets
• Ignoring two hundred years of well-documented psychical research presenting evidence contrary to neuroscientist dogmas
• Unreliable claims about fear or recall in rodents, produced by bad measurement methods such as "freezing behavior" estimations 
• Citations of poor-quality papers
• "Keep torturing the data until it confesses" tactics
• Constant reiterations of dogmas disproven or discredited by the facts observed by neuroscientists themselves
• Ignoring unusual case histories that conflict with "brains make minds" claims or "brains store memories" claims
• "Lying with colors" fMRI studies containing misleading visuals
• Lack of pre-registration
• Weird programming loop contortions of data, in which investigators senselessly think they have the right to programmatically perform arbitrary convolutions on the original data and still claim their twisted output is "what the brain is telling us"
• Professor pareidolia resembling "Jesus in my toast" claims
• Ignoring physical shortfalls of all brains that contradict or discredit some hypothesis being tested
• Unreliable and convoluted "spaghetti code" analysis
• Lack of detailed blinding protocols
• Ignoring observation failures
• Poor reproducibility
• HARKing 
• Not publishing null results
• P-hacking
• Title or abstract claims not matching results
• Lack of control subjects, or too few of them
• Blending real data and artificial (fake) data, with the fake data called "simulated" as if something "simulated" was better than something fake
• Unwarranted use of cell names and cell nicknames
• Unwarranted assumptions of causal effects, with a massive failure to consider reasonable alternative explanations of causes
• By a constant use of the passive voice rather than the active voice, a failure to document in most scientific papers the most basic facts needed to help police fraud, facts such as exactly who made an observation, the exact date when the observation was made, and where the observation was made. 

Today's professors of neuroscience do not get payments from snake oil manufacturers. But they often receive (directly or indirectly) very big financial benefits from pharmaceutical manufacturers and the manufacturers of biomedical devices, who may fund research or offer lucrative consulting fees for professors making claims that enhance the stock prices of such manufacturers.  

Just as I imagined Professors of Snake Oil Medicine creating some taboo against research challenging the effectiveness of snake oil, with such professors using techniques of gaslighting and slander to marginalize researchers producing such research, professors of neuroscience have used similar techniques to try to create some taboo against research challenging the "brains make minds" dogma. We have two hundred years of published research documenting the reality of spooky mental phenomena that cannot be explained by brains,  things such as ESP, clairvoyance, paranormal phenomena, apparition sightings, out-of-body experiences and near-death experiences. The professors of neuroscience have declared such research topics to be taboo, and have ignored all of the psychical research that defies their "brains make minds" dogma. Such professors have tried to gaslight, slander and marginalize respectable researchers producing such results that defy brains make minds" dogma.

The "smoke and mirrors" world of modern neuroscience resembles the "smoke and mirrors" shenanigans that would have gone on within Departments of Snake Oil Medicine if they had ever been established.  For a look at a recent example of some of this foolishness, read the recent "Mad in America" article here.  We read of a patient who would not give "informed consent" to a treatment risking her life, who was electrically shocked until she relented, with this being hailed as "restoring decision-making capacity," in some twisted mess of neurobabble. 

A recent article in The Atlantic is entitled "Science Is Drowning In AI Slop." We read that soon after the popular ChatGPT AI program was introduced a few years ago, there was a huge spike in submissions to science journals and science preprint servers. We can imagine how that worked:  scientists stuffing their papers with lots of AI-generated paragraphs and AI-generated charts. We read that some researchers who would rarely submit a paper to a journal are now submitting dozens per year. We read that scientists are running machine-learning algorithms on data, claiming to have produced some interesting outcome. The article calls this "a fraud template for AI researchers," noting "as long as the outcome isn't too interesting, few people, if any, will bother to vet it." 

I was for a long time a software developer, and I know how to read programming code. When I look at the programming code used for some of the low-quality "keep torturing the data until it confesses" neuroscience papers that I read, I will often see code that makes me think something along the lines of "no human would ever write junk this unreadable."  My guess is that neuroscientists are sometimes using AI-generated computer code to do black-box "analytics" on brain scan data and EEG data. I suspect that often both the code and the description of the code in the paper is AI-generated, and that often the author or authors do not even understand very well what the code is doing. The obscure output from such a thing is correctly described as AI slop. But a human peer reviewer may be unlikely to catch the nonsense, partially because (according to the article) much of peer review is now done by AI rather than humans. Another article about a popular scientific preprint server states, "Starting in early 2025, he says, the number of 'AI slop' submissions went up exponentially."

We read this, referring to the LLM algorithms used by AI programs:

"Other preprint servers have also seen a rise in poor, AI-generated content, Nosek says—including the generalist preprint service hosted by OSF, which stopped accepting any new submissions in August 2025 'because a majority of submissions were very low quality.'  PsyArXiv, a community-run psychology preprint server within OSF, swapped from moderating submissions after posting to moderating them in advance for the same reason.  Wijers says the new safeguard is needed in part because LLMs will improve, making it harder or impossible for moderators to distinguish fake AI-generated papers from legitimate work."

Papers Find Massive Image Chicanery or Dubious Doings in Neuroscience Studies

None of the "brains make minds" claims of neuroscientists depend on imagery, there being no photos that do anything to substantiate either the claim that brains make minds or that brains store memories, So someone might claim that the degree of fake or mislabeled images in neuroscience has no relevance to whether brains make minds.  But there is a possible relevance: if there are honesty problems or accuracy problems with many of the images used in neuroscience research, that is another reason for distrusting cognitive neuroscientists; and the less trustworthy cognitive neuroscientists are, the more we should doubt their dogmatic claims. 

A recent article at the Retraction Watch site (https://retractionwatch.com/) has the title "Widespread image reuse, manipulation uncovered in animal studies of brain injury." The "image reuse" being referred to is when some scientific paper has an image that it claims is data from new original research done by its authors, even though the image seems to be the same image published much earlier in some other scientific paper.  

We read this:

"More than 200 papers on ways to prevent brain injury after a stroke contain problematic images, according to an analysis published today in PLOS Biology...René Aquarius and Kim Wever, of the Radboud University Medical Center in the Netherlands, first noticed these patterns in 2023 when they started working on a systematic review of animal studies in the field...Of the 608 studies they analyzed, more than 240, or 40 percent, contained problematic images...At first, the pair tried to check the images manually, but the work was too slow. So they turned to ImageTwin, which cross-checks uploaded images against a database, making the process 'more efficient and accurate,'  Wever said.  The results showed a sprawling network of images that not only appeared in articles on early brain injury, but also showed up labeled under different experiment conditions across studies on Alzheimer’s disease, epilepsy and lung cancer, and other unrelated fields....In total, their analysis found 37 of these papers in research fields other than early brain injury. Overall, 133 of the 608 articles contained an image that also appeared in another publication, a pattern typical of paper mills or image reuse among an author group, Aquarius said...In their new publication, the researchers took a conservative approach to identifying image reuse, so Aquarius called the 40 percent estimate a 'best-case scenario.' "

The Retraction Watch article is based on the scientific paper here, entitled "High prevalence of articles with image-related problems in animal studies of subarachnoid hemorrhage and low rates of correction by publishers." That paper states this:

"Estimates of the prevalence of inappropriate image duplication in (biomedical) research remains uncertain and are dependent of the body of literature that is being investigated. Reports are sparse and cover widely different literature samples. Out of >20,000 articles from 40 scientific journals, 4% contained problematic figures [18], while Danish researchers detected inappropriate image duplication in 19% of preclinical depression publications [19]. Image-related issues were identified in 6.1% of the assessed articles published in Molecular and Cellular Biology [20] and in 16% of articles published in Toxicology Reports [21]. Finally, in a sample of articles published in the journal Bioengineered, >25% contained inappropriate image duplication [22]. A synthesis of the sparse data estimates the combined misconduct rate (including fabrication, falsification, and plagiarism) to be 14%, 1 in 7 research articles [23]. The 40% prevalence observed in our study far exceeds these figures, suggesting an alarming level of integrity issues in the preclinical subarachnoid hemorrhage literature."

Sometimes you have imagery malfeasance that is so common in some particular field that some researcher can use an "everybody does it" kind of defense. An example is what goes on in brain scan visuals that purport to show areas of "superior activation" in the brain. There massively occurs misleading visuals that give the impression that the amount of variation is much greater than it is. Typically the variation will be some very small amount such as 1 part in 200. But you will see visuals that try to make such differences look much greater than 1 part in 200. 

Page 68 of a scientific paper ("More Than Meets the fMRI: The Unethical Apotheosis of Neuroimages" by Eran Shifferman) has a quote talking about the kind of shady business that goes on when these visuals are produced:

"The time series of voxel changes may be motion-corrected, coregistered, transformed to match a prototypical brain, resampled, detrended, normalized, smoothed, trimmed (temporally or spatially), or any subset of these, with only a few constraints on the order in which these are done. Furthermore, each of these steps can be done in a number of ways, each with many free parameters that experimenters set, often arbitrarily. After preprocessing, the main analysis begins. . In a standard analysis sequence, experimenters define temporal regressors based on one or more aspects of the experiment sequence, choose a hemodynamic response function, and compute the regression parameters that connect the BOLD signal to these regressors in each voxel. This is a whole-brain analysis, and it is usually subjected to one of a number of methods to correct for multiple comparisons… the wholebrain analysis is often the first step in defining a region of interest in which the analyses may include exploration of time courses, voxelwise correlations, classification using support vector machines or other machine learning methods, across-subject correlations, and so on. Any one of these analyses requires making crucial decisions that determine the soundness of the conclusions."

After the quote, the paper author says, "This detailed description shows that BOLD-fMRI NIs [neuroimages] represent mathematical constructs rather than physiological reality (Burock 2009)." Page 70 of the same paper states this:

"A ubiquitous statistical error in functional neuroimaging is the non-independence error (aka double dipping): using the same data for selecting the voxels of interest and then using these voxels for the secondary analysis, the one upon which the functional conclusions are based9 . Double dipping violates random sampling because the test statistics are not inherently independent of the selection criteria of the region of interest, thus statistically guaranteeing the outcome of the second analysis and rendering them useless (Kriegeskorte et al. 2009; Vul et al. 2009). Similarly, as mentioned before, statistical tests in neighboring voxels are not independent of one another, because time series in neighboring voxels are intercorrelated (Peterson 2003). Analyses have shown that the non-independence error is widespread in BOLD-fMRI studies (40-50% of published papers) and that the severity of the distortions of the results presented in these papers could not be assessed. This necessitates replications and reanalysis (Kriegeskorte et al. 2009) or the results of these studies “mean almost nothing”, since they are 'using seriously defective research methods and producing a profusion of numbers that should not be believed' (Vul et al. 2009)."

On page 71 we have this complaint about the use of way-too-small study group sizes in brain scan studies:

"Yet another sizeable statistical concern is unfitting sample sizes: most published fMRI studies have sample sizes that would be considered exceedingly small by conventional standards (Yarkoni 2009; Button et al. 2013; Ingre 2013), if they include sample size calculations at all (Guo et al. 2014). It is established that in fMRI studies, small studies (n=16) fail to reliably distinguish small and medium-large effect sizes from random noise as do larger studies (n=100) (Ingre 2013). However, Wager et al. (Wager et al. 2009) report that across 415 fMRI studies reviewed, the average group size was smaller than 12, with some using only 4 subjects."

Page 73 refers to a "localization project," by which the author means attempts to show that particular brain regions activate more strongly when some type of cognitive activity is performed.  We read, "The cumulative effect of these types of data variability is a serious impediment on the localization project, suggesting that there are no macroscopic-level delineations corresponding to cognitive performance, and that they are probably a methodological artifact (Gonzalez-Castillo et al. 2012; Thyreau et al. 2012)."

The author of the scientific paper is apparently suggesting that you cannot actually find any evidence that particular regions of the brain are more active during particular cognitive activities. On page 76 the author refers to those running brain scan studies, saying, "This practice all too often amounts to unethical science, one where the generators of data overlook known shortcomings of their tools of the trade and press forward with producing claims too strong to be supported by exploiting the strong appeal of their meticulously crafted images."

Major Journal Suggests 3% of Biology Papers Look Like Paper Mill Junk

In three previous posts on this blog I discussed the issue of fraud in biology research. The posts were these:

• Study Suggests Massive Faking in Neuroscience Papers
• They're Calling It a Huge Memory Research Fraud, But Is It Only the Tip of the Iceberg?
• Fraud and Misconduct Are Not Very Rare in Biology

A recent article in the journal Nature asks "How big is science's fake-paper problem?"  We read this:

"An unpublished analysis shared with Nature suggests that over the past two decades, more than 400,000 research articles have been published that show strong textual similarities to known studies produced by paper mills. Around 70,000 of these were published last year alone (see ‘The paper-mill problem’). The analysis estimates that 1.5–2% of all scientific papers published in 2022 closely resemble paper-mill works. Among biology and medicine papers, the rate rises to 3%."

What's so bad if a scientific paper resembles the product of a paper mill? The article gives us a bit of a clue, without explaining it very well. It says, "Paper-mill studies are produced in large batches at speed, and they often follow specific templates, with the occasional word or image swapped." The average reader will have no idea of what this refers to, so let me explain. 

In computer programming a template is some body of text containing placeholders. The template can be used to make many different versions of a narrative, by simply replacing the placeholders with specific examples.  For example, the page here gives us a template for producing a press release announcing some scientific research. The template starts out like this:

"Scientists today announced that they are the first to successfully demonstrate SCIENTIFIC FINDING. This has long been one of the holy grails of SCIENTIFIC FIELD. 'This finding radically alters our understanding of the field, to say the least,' says FIRST AUTHOR, a SCIENTIFIC FIELDologist from INSTITUTION who led the research. 'We were stunned when we made the discovery. For a few minutes we just didn’t believe what we were seeing,'  says FIRST AUTHOR, then SECOND AUTHOR (a student of FIRST AUTHOR) yelled "We’ve done it!" and we started dancing around the LAB/OBSERVATORY/FIELD SITE. It was very exciting.”

If you are writing a scientific press release, you could manually replace the capitalized phrases to match some new research.  But templates such as these can also be inputs to computer programs. Computer programs can generate countless different versions of the narratives in a template, by doing search and replace of the capitalized words. 

So, for example, imagine you want 10,000 different versions of the story below:

"MALE HUMAN ONE had a good life, but he knew that something was missing. He tried using dating apps to meet Miss Right, but somehow it never worked out. But one day MALE HUMAN ONE had a stroke of luck.  He was at the BUSINESS PLACE ONE where he was a regular customer. He looked to his left, and was stunned by the beauty of a female he had never met before: FEMALE HUMAN ONE. MALE HUMAN ONE felt sure that he wanted to strike up a conversation with the beautiful stranger, but he couldn't think of what to say. He thought of saying TRITE OVERUSED PICKUP LINE, but thought that would never work.  Suddenly, he had a good idea. Walking up to the stranger he said, ORIGINAL WITTY ICE-BREAKING LINE." 

It would be very easy to write a computer program that generated 10,000 different versions of this story.  The computer program could just run in a loop, and thousands of times replace the phrases MALE HUMAN ONE, FEMALE HUMAN ONE and BUSINESS PLACE ONE with items randomly extracted from a list, or randomly generated. Similarly, the program could thousands of times replace TRITE OVERUSED PICKUP LINE with an item randomly chosen from a list of such lines, and replace ORIGINAL WITTY ICE-BREAKING LINE with  with an item randomly chosen from a list of such lines. 

It seems that paper mills are doing something similar, to generate phony scientific papers, which amount to phony narratives. We hear in the Nature article that some machine-learning software is being used to look for papers that are suspected products of paper mills. An estimate has been produced that 3% of the biology and medicine papers from recent years are fake papers produced by paper mills. This 3% figure is higher than for any of the other fields mentioned. We read this: "A June 2022 report by the Committee on Publication Ethics, based in Eastleigh, UK, said that for most journals, 2% of submitted papers are likely to have come from paper mills, and the figure could be higher than 40% for some."

Why would such wrongdoing occur? If you are a scientist living in a "publish or perish" culture, it may be expected that you will author a certain number of papers each year. There is an effect called publication bias, in which scientific journals prefer to publish papers reporting positive results. If you are a scientist doing experiments that have recently produced only null results, you may resort to paying some paper mill to get some result that will have a higher chance of getting published. The paper mill companies are typically in foreign countries, and have discreet names such as Suichow Editorial Services. 

A researcher named Bernhard A. Sabel has developed what he thinks is a pretty simple way to spot paper mill papers in biology and medicine: look for papers which have author email addresses that are private emails or hospital emails rather than college or university emails such as joesmith@harvard.com. The technique of Sabel is entirely different from the technique mentioned at the beginning of this post. 

The latest version of a paper by Sabel describes the paper mill industry:

"The major source of fake publications are 1,000+ 'academic support' agencies – so-called 'paper mills' – located mainly in China, India, Russia, UK, and USA (Abalkina, 2021; Else, 2021; Pérez-Neri et al., 2022). Paper mills advertise writing and editing services via the internet and charge hefty fees to produce and publish fake articles in journals listed in the Science Citation Index (SCI) (Christopher, 2021; Else, 2022). Their services include manuscript production based on fabricated data, figures, tables, and text semi-automatically generated using artificial intelligence (AI). Manuscripts are subsequently edited by an army of scientifically trained professionals and ghostwriters." 

Sabel mentions a case of a paper mill that emailed a scientific journal offering a sum of $1000 if the journal published one of the papers the paper mill (calling itself an editorial services firm) helped to produce. 

A paper by Sabel states this:

"More than 1,000 paper mills openly advertise their services on Baidu and Google to 'help prepare' academic term papers, dissertations, and articles intended for SCI publications. Most paper mills are located in China, India, UK, and USA, and some are multinational. They use sophisticated, state-of-the-art AI-supported text generation, data and statistical manipulation and fabrication technologies, image and text pirating, and gift or purchased authorships. Paper mills fully prepare – and some guarantee –publication in an SCI journal and charge hefty fees ($1,000-$25,000; in Russia: $5,000) (Chawla, 2022) depending on the specific services ordered (topic, impact factor of target journal, with/without faking data by fake 'experimentation')" 

Sabel estimates that paper mills are a major business, earning a revenue of about a billion dollars per year.  He estimates that close to 150,000 papers are questionable papers with red flags indicating possible paper mill authorship.  

It's so much easier when the "experiments" are all fake

I would imagine that experimental neuroscience papers are some of the easiest types of science papers for paper mills to fake. Many experimental neuroscience papers follow a very similar approach. It's as if very many experimental neuroscientists lack the imagination to think up new types of neuroscience experiment designs, and as if such neuroscientists are just borrowing the design structure from previous experiments (which often have very poor experimental designs).  With such repetition occurring massively, it is easier for paper mills to just detect some design pattern, and use it as a kind of cookie cutter, duplicating most of the text and giving it some novelty by using search-and-replace algorithms in which placeholder text is replaced with phrases chosen from a list.  

What is described above is an example of what can be called commodification corruption. Commodification is when something becomes a commodity to be bought, sold and exchanged in an economic system, and the term has a connotation of something that should not have become a mere commodity becoming a commodity. In the current "publish or perish" culture of academia, two of the biggest commodities may be paper counts (a number supposedly indicating how many papers a scientist has written) and citation counts (a number of times a scientist's papers have been cited).  These metrics are used to judge the performance of scientists. Lots of corruption is occurring in connection with such commodification. Such commodification corruption includes the following:

(1) A large fraction of neuroscientists are producing junk science papers guilty of Questionable Research Practices, with such "quick and dirty" studies occurring largely because an easiest path is being taken for a scientist to increase his "paper count" supposedly listing how many papers he has written. 

(2) Many scientists who did not materially participate in producing a scientific paper are being listed as co-authors (a practice sometimes called "guest authorship"), both to increase "paper counts" of scientists, and to increase the chance of a paper getting published. Such "guest authorship" gifts (given to professors in the same department as the real paper authors) are also given as a kind of bribe to increase the promotion prospects of those granting the gift. 

(3) Many scientists are engaging in appalling lying by claiming they authored some particular number of papers, when they were merely one of the listed authors of most of such papers. For example, if a scientist was the sole author of 10 papers, and was merely one of the authors of 50 other papers (50 papers having an average of 7 authors each), it is very misleading for such a scientist to describe himself as the author of 60 papers (his work being equivalent to being the sole author of merely about 17 papers). 

(4) Scientists are massively citing their own papers (a practice called self-citation), and are citing the papers of their friends or associates while expecting the favor to be returned, in an "old boy network" that can be described as "I'll rub your back if you rub mine." 

(5) Some scientists are paying paper mills (described above) to produce fake papers, for the sake of increasing their "paper count" supposedly listing how many papers they published. 

(6) Quite a few papers are being partially or mostly "ghost written" by employees of pharmaceutical companies or biotech companies, who are paid to produce results (accurate or not) that will tend to raise the stock price of such companies (such as claiming a success for one of the company's pills). Scientists listed as co-authors of such papers (who often did little or nothing to produce them) are often investors in such stocks, and stand to gain from both an increase in their "paper counts," and an increase in the value of their investments. 

Corruption almost inevitably follows commodification.  Something else that has been commodified in the world of science is the production of exaggerated or inaccurate science news stories that serve as clickbait that is highly profitable for parties such as web pages running ads on the web pages that you reach after clicking on some clickbait.  The corruption behind that is very large, and discussed here. We are now pretty much in a territory of "you can't trust the science news headlines," largely because of all the clickbait going on.  

Today on my I-Pad I am reading a story about a neuroscientist who supposedly received many millions in federal funding. The story suggests massive wrongdoing in his research, and suggests that scientists knew about this for years, but were reluctant to blow the whistle because they thought it might harm their careers. We can only imagine how much "turn a blind eye" stuff is going on to help enable some scientists to put fake paper-mill science articles on their resumes, and also poorly designed junk science articles or articles they were not involved in but were listed as co-authors. It sounds like what goes on in the movie industry, where people often turn a blind eye to "casting couch" abuses, not wanting to be called "trouble makers" after they blew a whistle by complaining to the press.