Peer review reduces a whole provenance trail — the observation, the evidence, the reasoning, the disagreement along the way — to a single opaque verdict. Breadcrumbs is infrastructure for making that trail legible again: from raw observation, through evidence and review, to attestation — for any contribution, not just a finished paper.
Breadcrumbs: physical attestations for ecosystem contributions
Hey atmospheric friends, over the weekend I started experimenting with physical attestations, as part of my exploration of an ATScience community labeller. breadcrumbs: physical objects — NFC tags, printed QR codes — that let someone attest to their own participation in something, or let an authority attest that someone did something specific. Two record types separate self-attested contributions (crumbs) from community-authored ones (badges). Lexicons: - `id.breadcrumbs.trail` — self-minted...
Introducing COSIG: The Collection of Open Science Integrity Guides
Investigating the integrity of published scientific papers is key to the scientific process, but the necessary knowledge is in short supply. We present COSIG, an open collection of meta-scientific guides enabling anyone to perform forensic peer review.
Open Evaluation: A Vision for Entirely Transparent Post-Publication Peer Review and Rating for Science
The two major functions of a scientific publishing system are to provide access to and evaluation of scientific papers. While open access (OA) is becoming a reality, open evaluation (OE), the other side of coin, has received less attention. Evaluation steers the attention of the scientific community and thus the very course of science. It also influences the use of scientific findings in public policy. The current system of scientific publishing provides only journal prestige as an indication of the quality of new papers and relies on a non-transparent and noisy pre-publication peer review process, which delays publication by many months on average. Here I propose an OE system, in which papers are evaluated post-publication in an ongoing fashion by means of open peer review and rating. Through signed ratings and reviews, scientists steer the attention of their field and build their reputation. Reviewers are motivated to be objective, because low-quality or self-serving signed evaluations will negatively impact their reputation. A core feature of this proposal is a division of powers between the accumulation of evaluative evidence and the analysis of this evidence by paper evaluation functions (PEFs). PEFs can be freely defined by individuals or groups (e.g. scientific societies) and provide a plurality of perspectives on the scientific literature. Simple PEFs will use averages of ratings, weighting reviewers (e.g. by H-factor) and rating scales (e.g. by relevance to a decision process) in different ways. Complex PEFs will use advanced statistical techniques to infer the quality of a paper. Papers with initially promising ratings will be more deeply evaluated. The continual refinement of PEFs in response to attempts by individuals to influence evaluations in their own favor will make the system ungameable. OA and OE together have the power to revolutionize scientific publishing and usher in a new culture of transparency, constructive criticism, and collaboration.
Reviewing post-publication peer review
Post-publication peer review (PPPR) is transforming how the life sciences community evaluates published manuscripts and data. Unsurprisingly, however, PPPR is experiencing growing pains, and some elements of the process distinct from standard pre-publication review remain controversial. I discuss the rapid evolution of PPPR, its impact, and the challenges associated with it.
Publication bias in the social sciences: Unlocking the file drawer
The file drawer is full. Should we worry? Experiments that produce null results face a higher barrier to publication than those that yield statistically significant differences. Whether this is a problem depends on how many null but otherwise valid results might be trapped in the file drawer. Franco et al. use a Time-sharing Experiments in the Social Sciences archive of nearly 250 peer-reviewed proposals of social science experiments conducted on nationally representative samples. They find that only 10 out of 48 null results were published, whereas 56 out of 91 studies with strongly significant results made it into a journal. Science , this issue p. 1502 , Fully half of peer-reviewed and implemented social science experiments are not published. , We studied publication bias in the social sciences by analyzing a known population of conducted studies—221 in total—in which there is a full accounting of what is published and unpublished. We leveraged Time-sharing Experiments in the Social Sciences (TESS), a National Science Foundation–sponsored program in which researchers propose survey-based experiments to be run on representative samples of American adults. Because TESS proposals undergo rigorous peer review, the studies in the sample all exceed a substantial quality threshold. Strong results are 40 percentage points more likely to be published than are null results and 60 percentage points more likely to be written up. We provide direct evidence of publication bias and identify the stage of research production at which publication bias occurs: Authors do not write up and submit null findings.
A Claude skill for pre-submission peer review
Open-source skill to use Claude + Consensus for pre-submission mock peer review backed by citations to published, peer-reviewed literature.
A New Paradigm for Scientific Publishing, Peer Review, and Impact Assessment
Scientific publishing and peer review have evolved little in three centuries, while the demands placed on them have grown profoundly. The growing role of artificial intelligence has underscored deep, systemic shortcomings of an aging system that has largely evaded innovation, a system whose origins are appallingly closer to the invention of the printing press than to the internet. We can do better – much better. This article is intended as the beginning of a communal experiment: a living document that critically reviews the modern academic publishing and peer-review system and presents a concrete framework to address what bibliometrics experts¹ have characterized as "the pervasive misapplication of indicators to the evaluation of scientific performance". Building on the Leiden Manifesto, DORA, and a body of scholarship spanning many disciplines and decades, we present a community-governed, non-profit platform organized around three trust-weighted impact factors, for articles, authors, and reviewers, with full algorithmic transparency, an open development log, and structural decoupling of credibility scoring from content moderation and from monetization. We invite the community to discuss, critique, and help shape it.
The Last Human-Written Paper: Agent-Native Research Artifacts
Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and the branching exploration process are discarded to fit a linear narrative; and an Engineering Tax, where the gap between reviewer-sufficient prose and agent-sufficient specification leaves critical implementation details unwritten. Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work. We introduce the Agent-Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine-executable research package structured around four layers: scientific logic, executable code with full specifications, an exploration graph that preserves the failures compilation discards, and evidence grounding every claim in raw outputs. Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an ARA Compiler that translates legacy PDFs and repos into ARAs; and an ARA-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste. On PaperBench and RE-Bench, ARA raises question-answering accuracy from 72.4% to 93.7% and reproduction success from 57.4% to 64.4%. On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities. Our code is open-sourced at https://github.com/Orchestra-Research/Agent-Native-Research-Artifact.
Screening, sorting, and the feedback cycles that imperil peer review
Scholarly journals rely on peer review to identify the science most worthy of publication. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors’ private sense of their work’s quality through their decisions of where to send their manuscripts. Second, journals’ reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism—wages—to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals must either solicit assistance from less qualified reviewers or ask current reviewers to do more; (3) as review accuracy drops, submissions further increase as more authors try their luck at venues that might otherwise be a stretch. We illustrate how this cycle is propelled by the increasing emphasis on high-impact publications, the proliferation of journals, and competition among these journals for peer reviews. Finally, we suggest interventions that could slow or even reverse this cycle of peer-review meltdown.
In an era where research evaluation methods are evolving, the Research Contribution Claim Network makes trustworthy tracking of non-traditional research output easy!
In this whitepaper, Patrick Hochstenbach (Ghent University Library), Thomas van Himbergen (SURF), Laurents Sesink (SURF) and Herbert Van de Sompel (DANS) introduce the...
You can just review things: A digital ethnography of informal peer review
Across scholarly communities, manuscripts face similar evaluative rituals: editors invite experts to privately assess submissions through formal peer reviews. This closed, loosely structured, and...
Something I keep thinking about is whether/how it would be possible to construct something like "trusted reviewing circles" without (1) destroying peer review's egalitarian goals, (2) accidentally enabling collusion rings, (3) recreating the same system with same issues over time.
Maria Antoniak
We are caught in such a trap. Asking good-faith community members to volunteer more when we can plainly see so much bad-faith behavior without consequences... IDK where it ends. Probably not central source of the problem, but NO ONE should be listed as an "author" on 20, let alone 40, submissions.
