#scientificjournals — Public Fediverse posts

I've been asked to peer-review a perspective article. I found some parts I think are inaccurate, that need further reflection or need to made clearer. Other parts I simply disagreed with or wanted to respond to. After discussing it with the editor, we agreed to split it into presenting the first types of issues in my peer-review (and hopefully have them improved), while I'll move the second type of issues into an opinion article where I respond to the original perspective article. Better for all parties, I think.

#PeerReview #ScientificPublishing #ScientificJournals

I've been asked to peer-review a perspective article. I found some parts I think are inaccurate, that need further reflection or need to made clearer. Other parts I simply disagreed with or wanted to respond to. After discussing it with the editor, we agreed to split it into presenting the first types of issues in my peer-review (and hopefully have them improved), while I'll move the second type of issues into an opinion article where I respond to the original perspective article. Better for all parties, I think.

#PeerReview #ScientificPublishing #ScientificJournals

I've been asked to peer-review a perspective article. I found some parts I think are inaccurate, that need further reflection or need to made clearer. Other parts I simply disagreed with or wanted to respond to. After discussing it with the editor, we agreed to split it into presenting the first types of issues in my peer-review (and hopefully have them improved), while I'll move the second type of issues into an opinion article where I respond to the original perspective article. Better for all parties, I think.

#PeerReview #ScientificPublishing #ScientificJournals

I've been asked to peer-review a perspective article. I found some parts I think are inaccurate, that need further reflection or need to made clearer. Other parts I simply disagreed with or wanted to respond to. After discussing it with the editor, we agreed to split it into presenting the first types of issues in my peer-review (and hopefully have them improved), while I'll move the second type of issues into an opinion article where I respond to the original perspective article. Better for all parties, I think.

#PeerReview #ScientificPublishing #ScientificJournals

I've been asked to peer-review a perspective article. I found some parts I think are inaccurate, that need further reflection or need to made clearer. Other parts I simply disagreed with or wanted to respond to. After discussing it with the editor, we agreed to split it into presenting the first types of issues in my peer-review (and hopefully have them improved), while I'll move the second type of issues into an opinion article where I respond to the original perspective article. Better for all parties, I think.

#PeerReview #ScientificPublishing #ScientificJournals

Publishing pays (off). How can public scientific knowledge be so exploited for profit?

Science is not just about making new discoveries.

Sharing results with the rest of the world via scientific journals is also a large part of a scientist’s work.

And nowadays, this involves a lot of money.

https://mediafaro.org/article/20260817-publishing-pays-off-how-can-public-scientific-knowledge-be-so-exploited-for-profit?mf_channel=mastodon&action=forward

#Science #Publishing #OpenAccess #Research #ScientificJournals #PrePrint

Publishing pays (off). How can public scientific knowledge be so exploited for profit?

Science is not just about making new discoveries.

Sharing results with the rest of the world via scientific journals is also a large part of a scientist’s work.

And nowadays, this involves a lot of money.

https://mediafaro.org/article/20260817-publishing-pays-off-how-can-public-scientific-knowledge-be-so-exploited-for-profit?mf_channel=mastodon&action=forward

#Science #Publishing #OpenAccess #Research #ScientificJournals #PrePrint

Publishing pays (off). How can public scientific knowledge be so exploited for profit?

Science is not just about making new discoveries.

Sharing results with the rest of the world via scientific journals is also a large part of a scientist’s work.

And nowadays, this involves a lot of money.

https://mediafaro.org/article/20260817-publishing-pays-off-how-can-public-scientific-knowledge-be-so-exploited-for-profit?mf_channel=mastodon&action=forward

#Science #Publishing #OpenAccess #Research #ScientificJournals #PrePrint

Publishing pays (off). How can public scientific knowledge be so exploited for profit?

Science is not just about making new discoveries.

Sharing results with the rest of the world via scientific journals is also a large part of a scientist’s work.

And nowadays, this involves a lot of money.

https://mediafaro.org/article/20260817-publishing-pays-off-how-can-public-scientific-knowledge-be-so-exploited-for-profit?mf_channel=mastodon&action=forward

#Science #Publishing #OpenAccess #Research #ScientificJournals #PrePrint

Publishing pays (off). How can public scientific knowledge be so exploited for profit?

Science is not just about making new discoveries.

Sharing results with the rest of the world via scientific journals is also a large part of a scientist’s work.

And nowadays, this involves a lot of money.

https://mediafaro.org/article/20260817-publishing-pays-off-how-can-public-scientific-knowledge-be-so-exploited-for-profit?mf_channel=mastodon&action=forward

#Science #Publishing #OpenAccess #Research #ScientificJournals #PrePrint

The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]

The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]

The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]

The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]

The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of current (and dramatic) example: as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of current (and dramatic) example: as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of example, as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of example, as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of current (and dramatic) example: as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

Global Changes in How Science is Done and Shared

Learn how AI and new open access rules change how scientists work. This affects how fast we find cures and solve big world problems like climate change.

#openscience, #researchai, #scientificjournals, #futureofscience, #datasharing

https://newsletter.tf/how-ai-and-open-access-change-scientific-research-2024/

Global Changes in How Science is Done and Shared

Learn how AI and new open access rules change how scientists work. This affects how fast we find cures and solve big world problems like climate change.

#openscience, #researchai, #scientificjournals, #futureofscience, #datasharing

https://newsletter.tf/how-ai-and-open-access-change-scientific-research-2024/

Scientists are moving away from private research. New data shows journals now do 73 different tasks to check facts, which is more than ever before.

#openscience, #researchai, #scientificjournals, #futureofscience, #datasharing

https://newsletter.tf/how-ai-and-open-access-change-scientific-research-2024/

Scientists are moving away from private research. New data shows journals now do 73 different tasks to check facts, which is more than ever before.

#openscience, #researchai, #scientificjournals, #futureofscience, #datasharing

https://newsletter.tf/how-ai-and-open-access-change-scientific-research-2024/

#Scientificjournals are facing a #crisis as #AIgeneratedcontent floods #submissions. This #AIslop includes fraudulent papers with fabricated data, images, and citations, making it difficult to discern genuine research. The problem is exacerbated by “paper mills” selling fake papers and the use of AI tools to generate submissions and peer reviews. https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/?eicker.news #tech #media #news

#Scientificjournals are facing a #crisis as #AIgeneratedcontent floods #submissions. This #AIslop includes fraudulent papers with fabricated data, images, and citations, making it difficult to discern genuine research. The problem is exacerbated by “paper mills” selling fake papers and the use of AI tools to generate submissions and peer reviews. https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/?eicker.news #tech #media #news

#Scientificjournals are facing a #crisis as #AIgeneratedcontent floods #submissions. This #AIslop includes fraudulent papers with fabricated data, images, and citations, making it difficult to discern genuine research. The problem is exacerbated by “paper mills” selling fake papers and the use of AI tools to generate submissions and peer reviews. https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/?eicker.news #tech #media #news

#Scientificjournals are facing a #crisis as #AIgeneratedcontent floods #submissions. This #AIslop includes fraudulent papers with fabricated data, images, and citations, making it difficult to discern genuine research. The problem is exacerbated by “paper mills” selling fake papers and the use of AI tools to generate submissions and peer reviews. https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/?eicker.news #tech #media #news

#Scientificjournals are facing a #crisis as #AIgeneratedcontent floods #submissions. This #AIslop includes fraudulent papers with fabricated data, images, and citations, making it difficult to discern genuine research. The problem is exacerbated by “paper mills” selling fake papers and the use of AI tools to generate submissions and peer reviews. https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/?eicker.news #tech #media #news

Videos from the first joint conference between the Médici, Mir@bel and Repères networks are now available on Canal-U!

On the theme of ‘Working together: networking, why and how?’, these meetings provided an opportunity to highlight the challenges and complementarity of our three networks, while promoting existing collaborations.
Thanks to Ambre Enault for posting this online.

https://www.canal-u.tv/chaines/medici/1res-journees-inter-reseaux-medici-reperes-mirbel-1-3-juillet-2025

#edition #documentation #scientificjournals

Videos from the first joint conference between the Médici, Mir@bel and Repères networks are now available on Canal-U!

On the theme of ‘Working together: networking, why and how?’, these meetings provided an opportunity to highlight the challenges and complementarity of our three networks, while promoting existing collaborations.
Thanks to Ambre Enault for posting this online.

https://www.canal-u.tv/chaines/medici/1res-journees-inter-reseaux-medici-reperes-mirbel-1-3-juillet-2025

#edition #documentation #scientificjournals

Videos from the first joint conference between the Médici, Mir@bel and Repères networks are now available on Canal-U!

On the theme of ‘Working together: networking, why and how?’, these meetings provided an opportunity to highlight the challenges and complementarity of our three networks, while promoting existing collaborations.
Thanks to Ambre Enault for posting this online.

https://www.canal-u.tv/chaines/medici/1res-journees-inter-reseaux-medici-reperes-mirbel-1-3-juillet-2025

#edition #documentation #scientificjournals

Videos from the first joint conference between the Médici, Mir@bel and Repères networks are now available on Canal-U!

On the theme of ‘Working together: networking, why and how?’, these meetings provided an opportunity to highlight the challenges and complementarity of our three networks, while promoting existing collaborations.
Thanks to Ambre Enault for posting this online.

https://www.canal-u.tv/chaines/medici/1res-journees-inter-reseaux-medici-reperes-mirbel-1-3-juillet-2025

#edition #documentation #scientificjournals

Videos from the first joint conference between the Médici, Mir@bel and Repères networks are now available on Canal-U!

On the theme of ‘Working together: networking, why and how?’, these meetings provided an opportunity to highlight the challenges and complementarity of our three networks, while promoting existing collaborations.
Thanks to Ambre Enault for posting this online.

https://www.canal-u.tv/chaines/medici/1res-journees-inter-reseaux-medici-reperes-mirbel-1-3-juillet-2025

#edition #documentation #scientificjournals

Here’s a wee puzzle: A mature Open Data focused journal (“Journal A”), owned and launched by an company or Institute (“Institute B”), developed into the flagship of an Academic Publisher (“Publisher C”), runs their own properly archived and citable blog with DOIs etc (“Blog D”).

If a briefly published editorial Blog Post (“Editorial E”) disappears from their Blog, could it be an accident, or something else?

https://blastedbio.blogspot.com/2025/10/the-case-of-missing-editorial-blog-post.html #AcademicChatter #ScientificPublishing #ScientificJournals #OpenData

Here’s a wee puzzle: A mature Open Data focused journal (“Journal A”), owned and launched by an company or Institute (“Institute B”), developed into the flagship of an Academic Publisher (“Publisher C”), runs their own properly archived and citable blog with DOIs etc (“Blog D”).

If a briefly published editorial Blog Post (“Editorial E”) disappears from their Blog, could it be an accident, or something else?

https://blastedbio.blogspot.com/2025/10/the-case-of-missing-editorial-blog-post.html #AcademicChatter #ScientificPublishing #ScientificJournals #OpenData

Here’s a wee puzzle: A mature Open Data focused journal (“Journal A”), owned and launched by an company or Institute (“Institute B”), developed into the flagship of an Academic Publisher (“Publisher C”), runs their own properly archived and citable blog with DOIs etc (“Blog D”).

If a briefly published editorial Blog Post (“Editorial E”) disappears from their Blog, could it be an accident, or something else?

https://blastedbio.blogspot.com/2025/10/the-case-of-missing-editorial-blog-post.html #AcademicChatter #ScientificPublishing #ScientificJournals #OpenData

Here’s a wee puzzle: A mature Open Data focused journal (“Journal A”), owned and launched by an company or Institute (“Institute B”), developed into the flagship of an Academic Publisher (“Publisher C”), runs their own properly archived and citable blog with DOIs etc (“Blog D”).

If a briefly published editorial Blog Post (“Editorial E”) disappears from their Blog, could it be an accident, or something else?

https://blastedbio.blogspot.com/2025/10/the-case-of-missing-editorial-blog-post.html #AcademicChatter #ScientificPublishing #ScientificJournals #OpenData

Here’s a wee puzzle: A mature Open Data focused journal (“Journal A”), owned and launched by an company or Institute (“Institute B”), developed into the flagship of an Academic Publisher (“Publisher C”), runs their own properly archived and citable blog with DOIs etc (“Blog D”).

If a briefly published editorial Blog Post (“Editorial E”) disappears from their Blog, could it be an accident, or something else?

https://blastedbio.blogspot.com/2025/10/the-case-of-missing-editorial-blog-post.html #AcademicChatter #ScientificPublishing #ScientificJournals #OpenData

Academic publishers’ billion-dollar business paywalling Estonian research | News https://www.byteseu.com/1390424/ #AcademicPublishing #Estonia #Libraries #Publishing #Research #ScientificJournals #universities

Which journal would you recommend to publish a review on the topic of #CognitiveMaps?
Criteria: non-profit, open access, good reputation

#ScientificJournals #OpenAccess #Neuroscience #Psychology #CognitiveMap

Which journal would you recommend to publish a review on the topic of #CognitiveMaps?
Criteria: non-profit, open access, good reputation

#ScientificJournals #OpenAccess #Neuroscience #Psychology #CognitiveMap

Which journal would you recommend to publish a review on the topic of #CognitiveMaps?
Criteria: non-profit, open access, good reputation

#ScientificJournals #OpenAccess #Neuroscience #Psychology #CognitiveMap

Which journal would you recommend to publish a review on the topic of #CognitiveMaps?
Criteria: non-profit, open access, good reputation

#ScientificJournals #OpenAccess #Neuroscience #Psychology #CognitiveMap

Which journal would you recommend to publish a review on the topic of #CognitiveMaps?
Criteria: non-profit, open access, good reputation

#ScientificJournals #OpenAccess #Neuroscience #Psychology #CognitiveMap

"A growing tide of fake papers is flooding the scientific record and proliferating faster than current checks can rid them from the system, scientists warn.

The source of the trouble is “paper mills,” businesses or individuals that charge fees to publish fake studies in legitimate journals under the names of desperate scientists whose careers depend on their publishing record.

The rate of fake papers generated by these operators roughly doubled every 1.5 years between 2016 and 2020, according to a study published Monday in the Proceedings of the National Academy of Sciences.

“The entire structure of science could collapse if this is left unaddressed,” said study author Luís Amaral, a physicist at Northwestern University.

Paper mills look for weak links, such as lax verification protocols, in the typically rigorous publication machinery, then exploit those to place hundreds of fabricated studies with vulnerable journals or publishers, according to scientist investigators who have been tracking and cataloging their work.

It can be a costly mess to clean up.

Publishers who have become aware of suspected paper mill activity have been forced to retract hundreds of papers at once, and in some cases shut down journals."

https://www.wsj.com/science/scientific-journals-fake-paper-mills-92e42230

#AI #GenerativeAI #AISlop #AcademicPublishing #PaperMills #ScientificJournals #Science #PeerReview

"A growing tide of fake papers is flooding the scientific record and proliferating faster than current checks can rid them from the system, scientists warn.

The source of the trouble is “paper mills,” businesses or individuals that charge fees to publish fake studies in legitimate journals under the names of desperate scientists whose careers depend on their publishing record.

The rate of fake papers generated by these operators roughly doubled every 1.5 years between 2016 and 2020, according to a study published Monday in the Proceedings of the National Academy of Sciences.

“The entire structure of science could collapse if this is left unaddressed,” said study author Luís Amaral, a physicist at Northwestern University.

Paper mills look for weak links, such as lax verification protocols, in the typically rigorous publication machinery, then exploit those to place hundreds of fabricated studies with vulnerable journals or publishers, according to scientist investigators who have been tracking and cataloging their work.

It can be a costly mess to clean up.

Publishers who have become aware of suspected paper mill activity have been forced to retract hundreds of papers at once, and in some cases shut down journals."

https://www.wsj.com/science/scientific-journals-fake-paper-mills-92e42230

#AI #GenerativeAI #AISlop #AcademicPublishing #PaperMills #ScientificJournals #Science #PeerReview

"A growing tide of fake papers is flooding the scientific record and proliferating faster than current checks can rid them from the system, scientists warn.

The source of the trouble is “paper mills,” businesses or individuals that charge fees to publish fake studies in legitimate journals under the names of desperate scientists whose careers depend on their publishing record.

The rate of fake papers generated by these operators roughly doubled every 1.5 years between 2016 and 2020, according to a study published Monday in the Proceedings of the National Academy of Sciences.

“The entire structure of science could collapse if this is left unaddressed,” said study author Luís Amaral, a physicist at Northwestern University.

Paper mills look for weak links, such as lax verification protocols, in the typically rigorous publication machinery, then exploit those to place hundreds of fabricated studies with vulnerable journals or publishers, according to scientist investigators who have been tracking and cataloging their work.

It can be a costly mess to clean up.

Publishers who have become aware of suspected paper mill activity have been forced to retract hundreds of papers at once, and in some cases shut down journals."

https://www.wsj.com/science/scientific-journals-fake-paper-mills-92e42230

#AI #GenerativeAI #AISlop #AcademicPublishing #PaperMills #ScientificJournals #Science #PeerReview

"A growing tide of fake papers is flooding the scientific record and proliferating faster than current checks can rid them from the system, scientists warn.

The source of the trouble is “paper mills,” businesses or individuals that charge fees to publish fake studies in legitimate journals under the names of desperate scientists whose careers depend on their publishing record.

The rate of fake papers generated by these operators roughly doubled every 1.5 years between 2016 and 2020, according to a study published Monday in the Proceedings of the National Academy of Sciences.

“The entire structure of science could collapse if this is left unaddressed,” said study author Luís Amaral, a physicist at Northwestern University.

Paper mills look for weak links, such as lax verification protocols, in the typically rigorous publication machinery, then exploit those to place hundreds of fabricated studies with vulnerable journals or publishers, according to scientist investigators who have been tracking and cataloging their work.

It can be a costly mess to clean up.

Publishers who have become aware of suspected paper mill activity have been forced to retract hundreds of papers at once, and in some cases shut down journals."

https://www.wsj.com/science/scientific-journals-fake-paper-mills-92e42230

#AI #GenerativeAI #AISlop #AcademicPublishing #PaperMills #ScientificJournals #Science #PeerReview

"A growing tide of fake papers is flooding the scientific record and proliferating faster than current checks can rid them from the system, scientists warn.

The source of the trouble is “paper mills,” businesses or individuals that charge fees to publish fake studies in legitimate journals under the names of desperate scientists whose careers depend on their publishing record.

The rate of fake papers generated by these operators roughly doubled every 1.5 years between 2016 and 2020, according to a study published Monday in the Proceedings of the National Academy of Sciences.

“The entire structure of science could collapse if this is left unaddressed,” said study author Luís Amaral, a physicist at Northwestern University.

Paper mills look for weak links, such as lax verification protocols, in the typically rigorous publication machinery, then exploit those to place hundreds of fabricated studies with vulnerable journals or publishers, according to scientist investigators who have been tracking and cataloging their work.

It can be a costly mess to clean up.

Publishers who have become aware of suspected paper mill activity have been forced to retract hundreds of papers at once, and in some cases shut down journals."

https://www.wsj.com/science/scientific-journals-fake-paper-mills-92e42230

#AI #GenerativeAI #AISlop #AcademicPublishing #PaperMills #ScientificJournals #Science #PeerReview

@ianRobinson @benlockwood

How did it come to that? A great, classic piece in #theGuardian.

#scientificPublishing #scientificJournals

https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science

@ianRobinson @benlockwood

How did it come to that? A great, classic piece in #theGuardian.

#scientificPublishing #scientificJournals

https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science

@ianRobinson @benlockwood

How did it come to that? A great, classic piece in #theGuardian.

#scientificPublishing #scientificJournals

https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science

@ianRobinson @benlockwood

How did it come to that? A great, classic piece in #theGuardian.

#scientificPublishing #scientificJournals

https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science

@ianRobinson @benlockwood

How did it come to that? A great, classic piece in #theGuardian.

#scientificPublishing #scientificJournals

https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science

I will never understand why the authors of a manuscript that they post on a preprint server spontaneously decide that it will be better for whoever reads their manuscript to have not only all the figures at the end, but also separated from the legends?

WHY 😭

(Same question for papers sent to review btw. Most journals allow for the format of your choice for the first submission. WHY not make it a nice, easily readable format??)

#ScientificJournals #ResearchPapers #Academia #Preprint #PeerReview

I will never understand why the authors of a manuscript that they post on a preprint server spontaneously decide that it will be better for whoever reads their manuscript to have not only all the figures at the end, but also separated from the legends?

WHY 😭

(Same question for papers sent to review btw. Most journals allow for the format of your choice for the first submission. WHY not make it a nice, easily readable format??)

#ScientificJournals #ResearchPapers #Academia #Preprint #PeerReview

I will never understand why the authors of a manuscript that they post on a preprint server spontaneously decide that it will be better for whoever reads their manuscript to have not only all the figures at the end, but also separated from the legends?

WHY 😭

(Same question for papers sent to review btw. Most journals allow for the format of your choice for the first submission. WHY not make it a nice, easily readable format??)

#ScientificJournals #ResearchPapers #Academia #Preprint #PeerReview

I will never understand why the authors of a manuscript that they post on a preprint server spontaneously decide that it will be better for whoever reads their manuscript to have not only all the figures at the end, but also separated from the legends?

WHY 😭

(Same question for papers sent to review btw. Most journals allow for the format of your choice for the first submission. WHY not make it a nice, easily readable format??)

#ScientificJournals #ResearchPapers #Academia #Preprint #PeerReview

I will never understand why the authors of a manuscript that they post on a preprint server spontaneously decide that it will be better for whoever reads their manuscript to have not only all the figures at the end, but also separated from the legends?

WHY 😭

(Same question for papers sent to review btw. Most journals allow for the format of your choice for the first submission. WHY not make it a nice, easily readable format??)

#ScientificJournals #ResearchPapers #Academia #Preprint #PeerReview