Conversation, Confrontation, & Recent Reads

June 15, 2026|Conference Catch-up, News Update

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This painting, The Family of Darius before Alexander, by Paolo Veronese (circa 1565–1570), depicts the family of the defeated Persian Emperor Darius III in audience with Alexander the Great and Hephaestion, Alexander’s closest companion and likely lover.

The Family of Darius before Alexander, Paolo Veronese, circa 1565–1570; photo taken at the National Gallery, London

**History tells us that Alexander was magnanimous and treated the Persian royal family with dignity, upholding their status. Alexander has won militarily, but in this painting my eye is drawn to the Persian royal family. Darius’s mother, Sisigambis, kneels before Hephaestion, mistaking him for Alexander. Every element (like a monkey on a chain) in a great work of art is there for a reason, and this mistake of identity can be interpreted, at least by me, as Veronese “throwing shade” on ostentatious power. Though both are the same age, Alexander is in flamboyant red and has a boyish look, while Hephaestion appears taller and more of a conqueror with is armor and shield.**

Sisigambis’ “mistake” is based on who has royal attitude. Again, history tells us that Alexander reportedly reassured her that she hadn’t erred because Hephaestion “was also an Alexander.” Alex and Heph are one and the same?

You might guess why this painting stands out to me today (when I took the photo in August of 2016 I didn’t know the context, I was struck by the imagery — and the monkey). Conquest is unstable. Western power enters the Persian court, but it doesn’t dominate it. Alexander in his flashy red suit may be the victor, but the painting shows him to be dependent on Persian acknowledgment. Not to put too fine a point on it, the painting reveals that Persia/Iran endures even when Western empires imagine they have mastered it.

And what about that monkey?

In this blog post: 1. my summary and analysis of the recent PurePub.ai online conference about AI in publishing — five themes across nine sessions; 2. information about the latest attack on academic freedom via new rules for government grants (plus a call to action!); and 3. some “Recent Reads” on peer review, a very cool openRxiv experiment, and an interesting ruling, out of the E.U., declaring Google responsible for misinformation in their AI overviews.

PUREPUB.AI CONFERENCE REPORT*

I really enjoyed the PurePub.ai online conference that took place a few weeks ago. Spanning two weeks, with one or two sessions a day, this free-to-attend event had an impressive line up of publishing and technology folks who engaged in deep conversations that went in surprising directions. I attended nine of the sessions, took detailed notes, and have put together my thoughts and ideas focusing on common themes as well as differences of opinion and approach. Following is my analysis with a few of my favorite quotes thrown in.

1. Trust is moving from an implicit editorial value to explicit infrastructure

“The challenge is trust. We need attribution, provenance, you need audits. We need all of this built into the agents.” Harsh Jegadeesan, Springer Nature

One of the clearest themes across the Pure Pub sessions was that trust can no longer remain an assumed feature of scholarly publishing. In an AI-mediated environment, trust has to become visible, structured, and usable by both people and machines. That means moving beyond broad assurances about peer review or editorial quality and instead we need to be building explicit signals around identity, provenance, attribution, correction status, data availability, audit trails, and human accountability.

“We are still in a world where we need to have a person responsible for the research that’s shared. And so we need to be able to validate that that is a real person.” Tracy Teal, openRxiv

Different sessions approached this from different angles. Some focused on community-based trust: the role of journals, societies, preprint servers, and disciplinary communities in deciding what is credible, relevant, and worth attention. Others focused on technical infrastructure: the need for machine-readable metadata, permissions, APIs, provenance records, and signed trust markers that can travel with content into AI systems. Still others focused on operational accountability: what it really takes to keep humans meaningfully involved, manage bias and false positives, verify AI outputs, and ensure that someone remains responsible when automated systems act.

“What should not happen is we just devolve all our responsibility to a machine, and then find out that when things go wrong… we look for who to blame.” Ravi Venkataramani, Kriyadocs

The common message was that AI does not make trust less important; it makes trust more difficult to maintain. If publishers do not make their trust signals clear and portable, those signals may be stripped away or replaced by opaque AI systems that decide authority on their own terms.

2. Human roles are not disappearing, but they are being redefined around judgment, taste, community, and accountability

“Journals are collections of communities who come together with a common purpose to try and understand a field or discipline.” Ian Mulvany, BMJ

Another strong theme across the Pure Pub sessions was that AI is not making people irrelevant; it is changing what human work is for. The panels moved past the simple “AI will replace humans” argument and focused instead on how work will be redistributed. AI may take on more of the mechanical parts of scholarly publishing, such as triage, checking, summarizing, formatting, reviewer discovery, metadata enrichment, prototype building, and even parts of peer review, but the human role shifts toward judgment, taste, accountability, community fit, and deciding what actually matters.

“In the enterprise environment, you need to manage agents in your staff environment… Know what your staff is doing and put guardrails.” Adam Hyde, pure.science

Different sessions emphasized different parts of that shift. Some focused on reviewer judgment, arguing that AI can help with objective checks but that novelty, significance, and scientific interpretation still require people. Others emphasized the community role of journals, societies, preprint servers, and editorial boards as places where fields decide what is credible, relevant, and worth attention. Several sessions focused on governance, warning that humans still need to define scope, guardrails, permissions, and accountability for agents and automated systems, especially within an organization. Others were more operational, cautioning that “human in the loop” cannot mean turning skilled staff into passive AI babysitters; people need to remain active participants in designing, testing, interpreting, correcting, and improving workflows.

“What human wants to be the AI babysitter?” Jeff Lang, Figure Two

Human responsibility is not going away. But the value of human work may become less about performing every task manually and more about providing meaning, context, judgment, and accountability in a system where machines can produce, process, and act at unprecedented scale.

3. AI intensifies the volume problem, but scaling throughput is not the same as improving scholarship

“Now we’re in a world of much increased volumes of content, but our attention span is the same. We don’t get any more time. And so therefore, the things that act as really good filters become more valuable in that world.” Ian Mulvany, BMJ

A third common thread across the Pure Pub sessions was that AI is a primary contributor to scholarly publishing’s volume problem. More submissions, more preprints, more AI-assisted manuscripts, more review outputs, more machine traffic, more tools, and more research artifacts are all coming at once. But the more interesting question was not simply how publishers can process all of this faster; it was whether processing more is actually the right goal.

“Maybe the answers to the problem might lie in the problem itself because ultimately AI might be one of the only things that can scale with itself.” Natalie Khalil, Reviewer3

Some sessions treated AI as a necessary response to scale: preprint servers need help screening submissions, editors need better ways to find reviewers, and platforms may need to handle huge increases in agent-driven traffic. Others focused on the hidden costs of that scale, noting that AI pilots create new work around training, governance, bias management, and system oversight. If papers become easier to produce, the published article may lose some of its value as a signal of expertise or originality. Researchers may be pushed to produce more rather than think more deeply, while publishers may be forced to invest in AI just to defend against fake papers, fake images, fake references, and paper mills. Several sessions pointed toward a different answer: AI should not just help the system absorb more volume; it should help identify what matters, surface patterns across the literature, improve verification, support better correction, and create better measures of quality.

“One of the benefits of kind of this flipped model that I was pitching is that you get everything, right? You get the whole corpus out there.” Katie Corker, ASAPbio

Scale is unavoidable, but abundance is not progress. The real challenge is making sure AI helps scholarship become more trustworthy, useful, and meaningful, not just faster and bigger.

4. The article, the platform, and the interface are all being unbundled for machines and agents

“They’re going to be going to their agent first and the agent is going to be the primary consumer and audience that you’re targeting.” Jonathan Woahn, Cashmere

A fourth major theme across the Pure Pub sessions was that the article, the platform, and even the user interface are starting to come apart. Scholarly publishing has long been built around human readers finding, opening, and reading articles, usually PDFs or web pages, on publisher platforms. But AI changes the audience. Machines and agents do not need a journal homepage, or a static PDF; they need structured content, metadata, permissions, provenance, APIs, connectors, and reliable ways to act on information.

“The AI model… should be surrounded by the deterministic control… like the identity permissions, or workflow state… validation.” Hong Zhou, KGL

Some sessions were excited by what this makes possible: richer research objects, interactive figures, audio-friendly content, computational articles, reusable data, and workflows where readers can explore evidence rather than simply read about it. Others were more focused on the architecture underneath: trusted backends, headless platforms, MCP-style connections, machine-facing endpoints, identifiable agents, and systems that preserve rules, rights, and workflow history while allowing new kinds of interaction on top. There was also a more cautionary approach. If content gets unbundled into data, claims, figures, summaries, and machine-consumable fragments, publishers risk losing the familiar places where their value has traditionally been seen: the journal, the article, the platform, and the branded reading experience.

“We’re heading towards a world where a lot of these platforms kind of get broken down into primitives… at an API layer, you’ve got… glorified databases with the business rules kind of enforced within them. But then… on top of that, we’re going to have progressively this kind of agentic layer.” Stuart Leitch, Silverchair

The PDF-and-platform model is no longer enough. The future will likely include multiple versions of content for different users, some for humans, some for machines, some for accessibility, some for verification, and some for reuse, and publishers will need to make sure that trust, context, attribution, and community value travel with all of them.

5. Publisher value and business models are being renegotiated around licensing, machine use, brand, and the “context layer”

“What their real value add is, and it’s not just the dissemination, right? It is having that curation in the trust layer.” William Gunn, Consultant

The final big theme was that AI is forcing publishers to rethink where their value lay. When machines become the readers, the summarizers, the workflow agents, traditional value-indicators like subscriptions, platform traffic, COUNTER usage, branded journal pages, PDF downloads, and human-only reading, are no longer as relevant.

“The AI model, the large language model, should not sit in the middle of the product — the magic, the black box. It should be surrounded by deterministic control. There’s no conflict; they have to collaborate together. By deterministic control, I mean identity permissions, workflow state, validation. This becomes even more important, and also the human oversight. It should be embedded into this current work architecture.” Hong Zhou, KGL

Across the sessions, there was a shared sense that publishers cannot rely on old business models. Some discussions focused on licensing and machine use: who gets to use publisher content, for what purpose, and under what terms. Others focused on visibility: if researchers get answers through AI tools without visiting publisher platforms, how do publisher brands, quality signals, and usage metrics survive? There was also a strong society-publisher thread, emphasizing that smaller and mission-driven publishers may not have the legal, technical, or financial capacity to respond as quickly as larger organizations, even though their community role remains highly valuable. Platform-focused discussions pointed toward new economics around tokens, agent traffic, and machine-facing services. Preprint and peer review discussions showed that AI also changes cost structures: more volume means more screening, more triage, and more pressure to decide which tools are worth paying for.

“We are still in a world where we need to have a person responsible for the research that’s shared. And so we need to be able to validate that that is a real person.” Tracy Teal, openRxiv

There were differences in perspective, some saw opportunity in new services and licensing models, some worried about losing the researcher relationship to AI platforms, and others saw sustainability challenges for societies, preprint servers, and smaller publishers. Publishers still have value, but they will need to make that value visible, portable, and useful in an AI-mediated ecosystem. Publisher value is trusted content surrounded by provenance, curation, attribution, licensing clarity, correction status, usage visibility, machine-readable trust signals, and accountable human judgment.

All in all

AI is forcing the industry to redefine what counts as content, peer review, trust, platform, journal, and publisher value. The most consistent split was not between AI optimists and AI skeptics. It was between workflow optimism and systems caution. Several speakers showed how AI can create new capabilities: faster prototyping, richer figures, efficient checks, upstream review, and scalable triage. Others repeatedly warned that those capabilities only help if the system also solves governance, provenance, incentives, accountability, brand visibility, correction, and community trust.

The attendee chat added an interesting perspective: participants were less interested in whether AI is impressive, and more interested in whether it will preserve human engagement, avoid reinforcing bad incentives, protect smaller publishers and societies, reliably expose trust signals, and above all, keep scholarship meaningful.

Thanks to Adam Hyde and the other organizers for putting together an amazing online conference!

**You can still experience all of the great conversations for a price.**

OMB OMG!

“Discretionary awards must, where applicable, demonstrably advance the President’s policy priorities”

Time Unveiling Truth, c. 1745, Giovanni Battista Tiepolo; photo taken at the Museum of Fine Arts, Boston

It was a little over a year ago that we first saw signs of a political takeover of science in the United States. In my February 24, 2025 blog post, *Truth vs Lies*, I listed the various Executive Orders issued by the new administration that were leading to “widespread disruptions in scientific and health research funding, affecting both domestic and international programs”. In that post I highlighted industry-wide reactions that included warnings of widespread funding cuts, concerns that researchers would withdraw from peer review and other editorial roles, worries that scientists would be forced to retract research articles that contained “forbidden terms”. I also included various strategies from industry organizations on how to combat or get around proposed rules.

I followed that up on April 11, 2025 with a post called Cronyism, Ideology, and the Fight for Academic Freedom, where I took conversations that had taken place in the scholarly publishing community (at conferences, in webinars and in other forums) post-executive orders, and I reflected on the precarious state of academic freedom in the U.S. I started that post out with a reference to a famous shipwreck; “In 1816, the French frigate Méduse ran aground off the coast of Mauritania. The shipwreck wasn’t caused by bad weather or a battle, but by the poor judgment of its captain who had been appointed for political reasons under King Louis XVIII. 147 people were stranded on a makeshift raft and after 13 days at sea only 15 survived, having endured starvation, insanity, and even cannibalism.” I concluded by asking if the U.S. was choosing ideology over inquiry, and short-term political gain over long-term global leadership.

Le Radeau de la Méduse, 1819, Théodore Géricault; photo taken at the Louvre, Paris

Here we are, just a bit over a year later, and we are seeing much of what was feared then, now very close to being codified into law. Issued on May 29, 2026, the U.S. Office of Management and Budget (OMB) announced a proposed new rule for managing government grants. The proposed OMB rule is not a routine update. It is a threat to the independence, openness, and effectiveness of federally funded science. If adopted it would give political officials greater influence over research funding, restrict international collaboration, and make it harder for scientists to share their work. All of this was predicted — we are definitely choosing ideology over inquiry!

1. Political control over grant review

The rule would allow senior political appointees to review grants before they are awarded and judge them against broad standards such as agency priorities, the “national interest,” and presidential policy goals (cited as “Gold Standard Science” multiple times). Peer review would remain, but only as advisory. This would weaken expert evaluation and make funding decisions vulnerable to political pressure. It would also allow agencies to suspend or terminate active grants with limited explanation, creating uncertainty for researchers and institutions.

2. Barriers to international collaboration

The rule would impose a “domestic-first” approach to research funding and restrict collaborations with certain foreign countries or entities. While national security matters, science depends on international cooperation, especially in fields such as climate, public health, agriculture, and emerging technologies. These restrictions could isolate U.S. researchers, slow discovery, and make it harder to address global problems that require global expertise.

3. Restrictions on conferences, societies, and publishing

The rule would limit the use of federal funds for conference attendance, professional society memberships, and publication costs, including open access fees and article processing charges. These are all core parts of the scientific process. Conferences enable feedback and collaboration. Professional societies sustain research communities. Publication costs support peer review, editorial work, preservation, and public access to results.

Taken together, these changes would make U.S. science more politicized, less collaborative, and less visible. They would undermine scientific freedom at a time when independent, open, and global research is urgently needed.

Following is additional information, including: 1. Links to the proposed rule change; 2. A CALL TO ACTION to submit public comment before July 13, 2026; and 3. A so-called explanation of what “Restoring Gold Standard Science” means.

Regulation for Federal Financial Assistance

You can read the full proposed rule change, and if you do be sure to read between the lines. Much of what is written sounds like common sense, but you can also find a political agenda hidden in plane site (like Gold Standard Science).

ACTION: Proposed rule.

SUMMARY: The Office of Management and Budget (OMB) proposes to revise the Guidance for Federal Financial Assistance to improve government-wide policies and requirements related to the management of grants, cooperative agreements, and other forms of assistance. OMB is proposing revisions that would improve transparency, accountability, and oversight for Federal awards across the Federal Government. This includes ensuring that American tax dollars are not wasted or misused, activities performed under Federal awards are consistent with law and policy, and recipients are held accountable when they fail to meet relevant standards. The revisions also aim to ensure that basic American principles of equality and equal opportunity are upheld throughout all stages of the award making process and that unlawful discrimination is no longer permitted. Proposed changes also include providing further clarification on the regulatory status of the OMB requirements and on the process for future updates to the government-wide requirements. Finally, OMB also proposes changes to reduce recipient burden. The listed Federal grant-making agencies propose conforming changes to their respective adopting regulations, or, in the case of some agencies and other entities, establishing new adopting regulations or policies. The proposed changes reflect the administration’s commitment to transparency, accountability, and proper oversight for the Federal grantmaking process. The proposed regulations seek to ensure that American tax dollars are ultimately used to serve the needs of the American public.

**Read the full document detailing the proposed rule change.**

TAKE ACTION!

Old Man Writing by Candlelight, Hendrick ter Brugghen, 1627; photo taken at the Smith College Museum of Art, Northampton, MA

Don’t like what you’re hearing!? Lodge a complaint! Make your opinion heard! Submit a public comment!

Comments are due on or before July 13, 2026.

*“*Late comments will be considered only to the extent practicable.”**

Be sure to provide details about your concerns as well as personal insights for how this proposed rule may affect you. Consider including:

How would you be harmed by the change?

How certain is it that harm will take place?

Did you make important life decisions based on the current rule?

What scientific capabilities would be lost if international collaboration was terminated?

What additional administrative burdens would this rule add in terms of time or money?

**SUBMIT A PUBLIC COMMENT**

Restoring Gold Standard Science

**Executive Orders, May 23, 2025**

For Sunday’s Dinner, William Michael Harnett, 1888; photo taken at the Art Institute, Chicago

“Gold Standard Science” is referred to multiple times in the proposed rule change, by it’s never defined. However, we can safely assume it refers to Section 3 of Executive Order 14303 which states,

“By the authority vested in me as President by the Constitution and the laws of the United States of America, including section 7301 of title 5, United States Code, it is hereby ordered: Section 1. Policy and Purpose. Over the last 5 years, confidence that scientists act in the best interests of the public has fallen significantly. A majority of researchers in science, technology, engineering, and mathematics believe science is facing a reproducibility crisis. The falsification of data by leading researchers has led to high-profile retractions of federally funded research.”

Of course it becomes political. The next paragraph states,

“Unfortunately, the Federal Government has contributed to this loss of trust. In several notable cases, executive departments and agencies (agencies) have used or promoted scientific information in a highly misleading manner.”

It continues to cite multiple, politically tinged examples related to COVID, climate change, coal, and DEI. On its face, Restoring Gold Standard Science sounds great — no one can disagree with Section 3 below — but reading between the lines, it’s clear that this is a set up for what we are now seeing with regard to the proposed rule changes announced May 29, 2026.

Sec. 3. Restoring Gold Standard Science. (a) Within 30 days of the date of this order, the Director of the Office of Science and Technology Policy (OSTP Director) shall, in consultation with the heads of relevant agencies, issue guidance for agencies on implementation of “Gold Standard Science” in the conduct and management of their respective scientific activities. For the purposes of this order, Gold Standard Science means science conducted in a manner that is: (i) reproducible; (ii) transparent; (iii) communicative of error and uncertainty; (iv) collaborative and interdisciplinary; (v) skeptical of its findings and assumptions; (vi) structured for falsifiability of hypotheses; (vii) subject to unbiased peer review; (viii) accepting of negative results as positive outcomes; and (ix) without conflicts of interest. (b) Upon publication of the guidance prescribed in subsection (a), each agency head, as necessary and appropriate and in consultation with the Director of the Office of Management and Budget (OMB Director) and the OSTP Director, shall promptly update applicable agency policies governing the production and use of scientific information, including scientific integrity policies, to implement the OSTP Director’s guidance on Gold Standard Science and ensure that agency scientific activities are conducted in accordance with this order. © Each agency head shall, to the extent practicable, incorporate the OSTP Director’s guidance on Gold Standard Science and the requirements of this order into the processes by which their agency conducts, manages, interprets, communicates, and uses scientific or technological information prior to the finalization of the updated policies under this section. (d) Within 60 days of the publication of the guidance prescribed in section 3(a), agency heads shall report to the OSTP Director on the actions taken to implement Gold Standard Science at their agency.

And this just in from the New England Journal of Medicine!

The OMB and the Politicization of Science

**The Editors, New England Journal of Medicine, June 15, 2026 **“The rule changes that the White House Office of Management and Budget (OMB) recently proposed to the Health and Human Services (HHS) grant process would drastically change this process. There are too many objectionable aspects of these proposals to discuss here. But three are particularly striking. Political appointees would be able to make funding decisions and could ignore the advice of independent scientists. They could also stop funding midway through the promised grant period. And they would institute new rules, including rules severely limiting foreign interactions. These changes raise fundamental concerns about the research endeavor, about ethical obligations to participants in clinical studies, about the ability to train the next generation of leaders in biomedical innovation, and about society’s ability to address disease threats early, before broader consequences occur.”

RECENT READS

Tough peer-review process? Your paper might end up being more highly cited

An AI-led analysis of publicly available peer-review reports links requests for major revisions with papers that end up having high impact.

Mariana Lenharo, *Nature*, May 20, 2026

“The study, posted to the arXiv preprint server last month1, evaluated the peer-review correspondence associated with a selection of papers published in Nature Communications. In 2016, the journal started making these files public for some of the papers that the journal had accepted and, since 2022, it does so for all accepted articles. It does this for transparency and to inform discussion of published papers in the research community, the journal says.

“Preprint co-author An Zeng, who is a specialist in complexity science at Beijing Normal University in China, says that he and his colleagues “thought these files could tell us a lot about the ‘negotiation’ between reviewers and authors” to get papers published.”

Demanding peer review is associated with higher impact in published science

Huihuang Jiang, Heyang Li, Zifan Wang, Ying Fan, An Zeng, *arXiv*, April 15, 2026

“Peer review shapes which scientific claims enter the published record, but its internal dynamics are hard to measure at scale because reviewer criticism and author revision are usually embedded in long, unstructured correspondence. Here we use a fixed-prompt large language model pipeline to convert the review correspondence of \textit{Nature Communications} papers published from 2017 to 2024 into structured reviewer — author interactions. We find that review pressure is concentrated in the first round and focused disproportionately on core claims rather than peripheral presentation. Higher average opinion strength is also associated with more reviewer disagreement, while review patterns vary little with broad team attributes, consistent with relatively impartial evaluation. Contrary to the intuition that stronger papers should pass review more smoothly, with greater reviewer — author agreement and less extensive revision, we find that stronger criticism, higher-quality comments, and greater revision burden are associated with higher later citation impact within accepted papers. We finally show that fields differ more in review style than in review length, pointing to disciplinary variation in how criticism is negotiated and resolved. These findings position open peer review not just as a gatekeeping mechanism but as a measurable record of how influential scientific claims are challenged, defended, and revised before entering the published record.”

Biology Open paid peer review model cuts times to five working days

***Research Information*, June 5, 2026**

“A paid-reviewer model trialled by The Company of Biologists on its Biology Open journal has reduced average peer review times from more than seven weeks to just over five working days.

“The Fast & Fair peer review model, first piloted in 2024, uses a pool of pre-contracted reviewers who receive payment only if they complete reviews on time and meet editorial quality standards. Following the success of the initial trial, Biology Open expanded the approach in April 2025, applying it to all direct submissions where suitable reviewer expertise was available. Under the model, reviewers were paid £220 per manuscript, conditional on both timely completion and editorial assessment of review quality.

“The results suggest a substantial improvement in review speed and reviewer engagement. Among peer-reviewed manuscripts submitted in 2025, the average time to a first decision based on completed reviews fell from 37.7 working days under conventional peer review to 5.5 working days using Fast & Fair.”

Expanded implementation of Fast & Fair paid peer review reduces time to first decision without reducing review quality in a biology journal

Daniel A Gorelick, Alejandra Clark, *bioRxiv*, June 3, 2026

“Traditional peer review is often slowed by delays in identifying willing reviewers and waiting for completed review reports. In a 2024 pilot on the journal Biology Open, we showed that Fast & Fair peer review, which uses pre-contracted paid reviewers and a structured editorial timeline, could deliver rapid, high-quality peer. Here, we report the expanded implementation of Fast & Fair at Biology Open in 2025. From 1 April 2025 onward, all direct submissions to the journal were considered for Fast & Fair peer review unless appropriate pre-contracted reviewer expertise was unavailable. Reviewers were paid 220 GBP per manuscript only if they completed the review on time, and the review met editorial quality expectations.

Among peer-reviewed manuscripts submitted in 2025, Fast & Fair reduced time to first decision with reviews from a mean of 37.7 working days under conventional peer review to 5.5 working days. Reviewer commitment also improved. Fast & Fair invitations were accepted more often than conventional invitations (67% versus 23%), had lower nonresponse (13% versus 39%), and had higher completion among accepted invitations (98% versus 87%). Faster review was not associated with reduced review quality. Handling editors scored each review for usefulness in editorial decision-making. Fast & Fair produced fewer low-scoring reports than conventional peer review. Editorial behavior was also unchanged, with similar first-decision profiles and final acceptance rates (59% versus 61%). While financial sustainability remains to be tested at scale, the Fast & Fair model addresses a major bottleneck in traditional peer review by replacing ad hoc reviewer recruitment with conditional compensation, predefined quality standards and a strict editorial timeline.”

Try our first openRxiv Labs experiment: a new interactive reading experience with Curvenote Reader

**openRxiv News, June 11, 2026**

“Growing directly from the strong foundation bioRxiv and medRxiv have built over the past decade, we wanted to create an experimental space for pushing the boundaries of research communication on top of this corpus of preprints, figures, metadata, and other research outputs. Last week we launched openRxiv Labs as a structured program for testing new and ambitious approaches to research communication.

“Today we’re excited to launch our first experiment, Curvenote Reader in openRxiv Labs — a new way to experience the 350,000+ preprints in our corpus. We are thrilled to partner with Curvenote, an organization that shares our commitment to open science and aligns with our Labs partner criteria on this first experiment.

“Find a preprint through search, or use a DOI. The URL structure is the same as bioRxiv: https://reader.openrxivlabs.org/content/DOI, e.g. https://reader.openrxivlabs.org/content/10.64898/2026.06.04.730096v1

“We’re starting with bioRxiv preprints, offering readers the ability to explore references, terminology, expanded figures, and related works while staying in the context of the original preprint. Over time, we’ll expand to include medRxiv preprints, iterate on the experience to allow interested authors to add even more interactive elements, and explore enriching the interface with additional features.”

**Check out Curvenote Reader in openRxiv Labs**

Landmark German ruling declares Google’s AI Overviews are Google’s own words and makes it liable for false answers

Matthias Bastian, *The Decoder*, June 11, 2026

• A German regional court has ruled that Google is directly liable for false claims in its AI-generated search overviews.
• In this case, Google’s AI had wrongly linked two publishers to scams and shady business practices.
• The court treated the AI overviews as Google’s own content and rejected Google’s argument that users were responsible for fact-checking the results themselves.

“AI overviews aren’t search results

“Google’s AI overviews work nothing like traditional search results, the court argues. The AI rewrites and judges results ‘in its own words and according to its own structure,’ the ruling says. In the case at hand, for example, it opened with confident claims like ‘Yes, [company] is known for dubious business practices,’ then built its own structure with a summary, red flags for the alleged scam, and tips for users.

“The court also found that the AI overview made claims ‘that are not even made in the search results.’ None of the linked sources drew any connection between the plaintiffs and the shady companies the AI mentioned. The court called these ‘the defendant’s own statements.’

“Google built the AI, Google offered it to users, so Google owns what it produces, ‘because it alone has influence over the AI’s offering and the algorithms with which the AI operates.’”

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**Scholarly Publishing Solutions is a consulting team with decades of experience leading, growing, and modernizing scholarly publishing organizations. Founded by Jennifer Deyton and Tony Alves, SPS focuses on peer review and editorial workflows, including policy development, best practices, systems design, research integrity, workflow improvement, and the practical use of AI.**

Jenn and Tony bring complementary expertise across the operational, strategic, and technology sides of scholarly publishing. Jenn offers deep experience in editorial operations, society publishing, scalable service delivery, and organizational growth, including the discipline that comes from building a small editorial services operation into a mature, successful company. Tony brings broad product and systems experience across the publishing stack, including interoperability, platform strategy, standards development, AI implementation, and research integrity tooling ecosystems.

Together, they help publishers understand what is working, identify what needs to change, and design practical solutions that can be implemented in real-world editorial and publishing environments. Their approach helps organizations improve efficiency, strengthen trust, support sustainable growth, and modernize workflows without losing sight of the values and relationships that make scholarly communication work.

**Contact us at info@ScholarlyPublishingSolutions.com**

*Disclosure: All original content has been compiled, analyzed, and edited by me. AI has been used to organize my notes, compose parts of early drafts, and to copyedit.

Copyright 2026, Tony Alves, Scholarly Publishing Professional. All Rights Reserved.