Pith. sign in

REVIEW 4 major objections 5 minor 114 references

Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that scholarly validation should be rebuilt as public, identity-linked commentary on append-only ledgers, with AI synthesizing the conversation as it unfolds.

desk verdict A position paper with good instincts on null results but no empirical grounding; the identity/candor tension is conceded by the author, and invalid DOIs make the citations untrustworthy. read the letter →

arxiv 2506.22497 v1 pith:VX7VQLMB submitted 2025-06-25 cs.CY cs.AIcs.DLcs.SIphysics.hist-ph

Craig Steven Wright This is my paper · ORCID
classification cs.CYcs.AIcs.DLcs.SIphysics.hist-ph
keywords peerreviewreformidentity-linkedcommentaryblockchainaudittrailAIsynthesisnullresultsretractionsreputationsystemsopenscience
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Peer review today is a secret, binary gatekeeping step: a few anonymous reviewers decide accept or reject, and publication ends the conversation. This paper argues that validation should instead be a continuous, public dialogue, with every review, correction, retraction, and replication signed by a persistent identity and recorded on an immutable ledger. The motive is epistemological: hidden reviewers cannot be held accountable, null results and retractions are suppressed, and knowledge is frozen into static credentials. The paper proposes a concrete architecture—blockchain for tamper-proof records, AI for summarisation and contradiction detection, and a learned trust function that weights critiques by each reviewer's track record—and claims this would make scholarship more accurate, traceable, and self-correcting.

What carries the argument

The load-bearing object is the identity-linked commentary graph $G=(V,E)$: nodes are persistent scholarly identities and artefacts, and directed, typed edges are signed commentaries, replication signals, and citation events, all anchored to a blockchain (the paper specifies the original Bitcoin protocol as preserved in Bitcoin SV). An append-only ledger makes every assertion immutable; AI agents operate on the graph to summarise, detect contradictions and overlaps, score novelty, and compute trust weights; versioning ledgers turn corrections into traceable first-class events. The machinery works together to convert review from a one-time private judgment into a continuous, machine-readable public computation of scholarly value.

What would settle it

Run a field experiment in one discipline where the same submissions are reviewed twice, once with reviewer identities hidden and once with them attached, and measure whether signed reviews are measurably less candid when the submission comes from a powerful author; if public identity systematically suppresses legitimate critical content, the central claim fails even if the whole blockchain infrastructure works as described.

Watch

Extended reading notes

Core claim

The central claim is that replacing the binary filter $F : P \to \{0,1\}$ of accept/reject with a time-indexed commentary function $C : A \times T \to E$ will produce a more accurate and accountable scholarly record. Each commentary $e = (k_\alpha, \tau, \varphi)$ carries a reviewer's public key, a timestamp, and a structured evaluation payload; it is hashed onto an append-only chain, so it can never be deleted or posted anonymously. The epistemic weight of a critique becomes $w(\varphi) \propto f(H_\alpha)$, where $f$ is a learned trust function over the reviewer's history of reviews, citation relevance, and error detection rate. In this scheme publication is the start of integration, not the end: retractions and null results are first-class scholarly outputs, citations become typed and verifiable ledger events, and reputation is earned by correction and replication rather than by editorial acceptance.

Load-bearing premise

The load-bearing premise is that making reviewers publicly identifiable will, on balance, reduce trolling and careless reviews without chilling candid criticism or inviting harassment; the paper itself acknowledges in its ethics section that anonymity protects whistleblowers and dissenters, but it never models or resolves that trade-off.

Editorial extensions

If this is right

  • A paper's standing becomes a time-dependent integral of commentary, replication, and reuse signals, so a modest initial reception can be revised upward or downward as evidence accumulates.
  • Null results and retractions become citable, credit-bearing outputs, which would rebalance the current publication bias toward positive findings.
  • Reviewers become visible intellectual contributors whose reputation rises or falls with the later fate of their critiques.
  • Citations are recorded as typed, timestamped signatures, letting AIs separate support from critique and trace cross-disciplinary method reuse.
  • Interdisciplinary work is no longer punished for failing one field's canon, because validation comes from distributed commentary across domains.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the same ledger architecture could support pseudonymous reviewers whose identity keys are held by an independent authority, giving whistleblower protection while keeping abuse traceable; the paper flags the need but leaves the mechanism unspecified.
  • Inference: the learned trust function $f(H_\alpha)$ depends on knowing which past reviews were correct, and those labels would likely come from the same community whose behaviour is being scored, creating a circularity that a practical deployment would have to break with independent replication outcomes.
  • Inference: a small pilot that compares signed versus anonymous review on the same manuscripts, and tracks candor, harassment complaints, and later corrections, would directly test the paper's core behavioural premise before any blockchain is built.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes replacing conventional pre-publication peer review with a system of open, identity-linked, append-only commentary anchored in blockchain infrastructure and AI-assisted synthesis. It argues that this will improve reviewer accountability, reduce trolling, integrate null results and retractions as first-class contributions, enable dynamic reputation, and make scholarly knowledge a traceable, dialogical process. The paper is written as a design manifesto: it critiques current gatekeeping, introduces formal notation for commentary traces and trust functions, discusses cross-disciplinary applications, and closes with governance and ethical considerations.

Significance. The paper is a broad and ambitious vision with real strengths: it identifies genuine pathologies of the current peer-review system, including publication bias, delayed dissemination, and the suppression of null results, and it explicitly values retractions, correction, and post-publication critique as epistemic contributions. It also brings philosophical frameworks (Habermas, Popper, Freire, Fricker) into contact with technical mechanisms, which could be valuable framing for future work. However, the manuscript is a proposal, not a demonstrated result: it ships no prototype, no data, no machine-checked proofs, and no falsifiable predictions. Its formal apparatus largely restates the proposal in symbols, and its central behavioral premise—that identity-linked public review improves accuracy without chilling dissent—is asserted rather than tested. The paper is therefore significant as a synthesis and a research agenda, but its claims of feasibility and benefit are not currently supported.

major comments (4)
  1. [§8.1, §10.3, §2.1] Sections 8.1 and 10.3 present a direct, unresolved tension. §8.1 relies on Cheng et al. (2017) and Bernstein et al. (2011), which study online commenting rather than academic peer review, to argue that public identity disincentivizes trolling. §10.3 then concedes that "anonymity has historically protected whistleblowers and dissenting voices" and that removing it "may suppress necessary but unpopular interventions." No model, data, or design mechanism is given to reconcile these effects. This is load-bearing because the benefit case in §2.1 depends on a trust function f(Hα) learned from candid, identity-linked commentary; if public identity chills dissent, the training corpus is self-censored and the claimed epistemic weighting loses validity. The authors should either add a governance layer with protected pseudonymity or present a formal or empirical analysis of the trade-off.
  2. [§2.1, §3.2, §3.3, §7.3] The formal apparatus is decorative rather than demonstrative. Equations such as w(ϕ) ∝ f(Hα) in §2.1, A′(t) = A + Σwiϕi in §3.2, WA(t) = ∫f(ϕ,r,ρ)dτ in §3.3, and ∇R(H,t) in §7.3 are not derived from a model and have no operational counterparts: f, wi, ϕ, r, ρ, and R are not specified with measurement procedures, data sources, or identifiability conditions. In particular, A′(t) = A + Σwiϕi is not a well-defined vector operation because A and ϕi belong to different spaces. These equations restate the proposal in symbols; they do not establish feasibility or correctness.
  3. [§8.1, §4 (overall)] The manuscript does not engage the extensive empirical literature on open and identity-linked peer review, which has found that open review can reduce willingness to review, discourage junior researchers, and shift comment sentiment toward the positive. The evidence cited in §8.1 comes from anonymous online forums, not academic peer review, so the extrapolation is weak. A concrete test—for example, a randomized comparison of review content under anonymous, signed, and pseudonymous conditions—or a formal model of participation and candor is needed before the central claim that public identity improves accuracy can be accepted.
  4. [§7.4, §10.3, §3.1] The proposed system lacks a failure-mode analysis for its own reputation mechanism. Because all reviews are permanent and identity-linked, a reviewer cannot retract a mistaken critique, and the proposed reputation gradient amplifies high-status voices even when they are wrong. The manuscript does not describe how a reviewer's error is corrected, how harassment or doxing is prevented, or how protected dissent is enabled. Section 10.3 lists privacy, defamation, and whistleblower concerns but does not explain how the architecture mitigates them. This is load-bearing because the system's claimed advantage over anonymous review depends on the benefits of identity-linked commentary outweighing these new risks, and no such weighting is provided. In §3.1 the paper also concedes that formal academic studies on Bitcoin SV in this area are "emerging," which is difficult to reconcile with the strong infrastructure claims made in §5.1.
minor comments (5)
  1. [§8.2, References] The Ross-Hellauer citation is given as [1998] with a Nature page range; the widely cited open-peer-review work by this author is from 2017. Please verify the reference and correct the year and venue if needed.
  2. [§12.1] The final paragraph contains a stray closing quotation mark after the phrase "the medium itself." Please remove the formatting artifact.
  3. [§2, §3, §6] Mathematical notation is used inconsistently across sections (for example, E : P → Rn, V : (P,R) → B, and C : A×T → E), and many symbols are never explicitly defined. A glossary and numbered equations would improve precision.
  4. [§6.2] The contradiction operator K(Ai,Aj) is defined using "¬ψ∈Fj" without specifying the language or domain in which ψ is interpreted; the formal definition is therefore underspecified.
  5. [§7–§11] The manuscript is very long and repetitive; condensing sections 7 through 11 and moving some philosophical discussion to a clearly marked outlook section would improve readability without losing the main argument.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is a normative design proposal with illustrative formalisms, not a prediction or derivation that reduces to its own inputs.

full rationale

The paper makes no empirical predictions and contains no fitted parameters that are then relabeled as results. Its central formal objects—such as w(φ) ∝ f(Hα), the epistemic loss L = n·pm·q, and the trust update T_{t+1}^a = T_t^a + λ1·endorsements − λ2·flagged_errors + λ3·replication_support—are illustrative definitions of a proposed system, not derivations from data. The claimed benefits of identity-linked review are supported by external empirical literature (e.g., Cheng et al. 2017 on anonymity and trolling) and are explicitly acknowledged as in tension with the protective value of anonymity in §10.3, where the paper concedes that removing anonymity 'may suppress necessary but unpopular interventions.' That is an unresolved empirical or normative concern, not a circularity. The paper does not import a uniqueness theorem from the author's prior work; it does not cite the author's own publications as load-bearing evidence; and it does not rename a known empirically fitted result as a derivation. Its formulas are presented as notation for a proposed architecture rather than as outputs of a derivation chain, so there is no step at which an equation equals its own input by construction. For these reasons, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 3 invented entities

The central claims rely on assumptions about human behavior under public identity, the stability and suitability of the Bitcoin SV ledger, the reliability of AI synthesis, and the ethics of mandatory identity. The paper also introduces several proposed metrics (commentary trace, reputation gradient, review quality index) with no measurement method. The many tunable weights (α, β, γ, λ_i, ε_s, ε_m) are placeholders rather than fitted parameters, so the free-parameter list is empty.

assumptions (5)
  • domain assumption Anonymity is a principal cause of low-quality peer review and trolling.
    Assumed in §2.1 and §8.1, citing Cheng et al. 2017; the paper does not weigh evidence for the benefits of anonymized review or the chilling effects of public identity.
  • domain assumption Bitcoin SV provides a fixed, stable, low-cost, scalable protocol suitable for global scholarly recording.
    Assumed in §3.1 and §5.1; the assertion that BSV has a fixed protocol rule set ignores its contentious fork history and contested leadership, so the premise is not established.
  • domain assumption AI agents can reliably summarize, detect contradictions, and estimate novelty across disciplines without significant error.
    Assumed in §6; no evidence or error analysis is given for these capabilities in scholarly contexts.
  • ad hoc to paper Persistent public identity can be required without unacceptable harm to privacy, dissent, or whistleblowing.
    The paper wavers: §10.3 admits anonymity protects whistleblowers but the framework otherwise mandates linked identity for all commentary; no resolution is offered.
  • domain assumption Scholarly value can be represented by weighted integrals or aggregates of commentary, replication, and reuse signals.
    Postulated in §3.2 and §3.3 (e.g., A'(t)=A+Σwi·φi, W_A(t)=∫...); no justification that such a scalar exists or is meaningful.
invented entities (3)
  • Commentary trace as a weighted epistemic object A'(t)
    purpose: Defines the 'evolving object' that replaces static publication; used to argue that publication is the start, not end, of validation.
    Introduced in §3.2; a formal object with no empirical measurement procedure.
  • Reputation gradient ∇R(H,t)
    purpose: Measures temporal evolution of claim credibility; used to argue historical claims should decay or grow.
    Defined in §7.3; no data or validation of the gradient's computation.
  • Review Quality Index (RQI)
    purpose: A composite score for reviewer contributions, combining text analysis and ex post outcomes.
    Proposed in §10.1 as a 'composite review quality index'; no implementation or user study is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship." pith.science (2026). https://pith.science/paper/VX7VQLMB

@misc{pith2026250622497,
  author       = {Pith},
  title        = {Pith review of: Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VX7VQLMB}},
  note         = {Machine review of arXiv:2506.22497}
}
read the original abstract

This paper reconceptualises peer review as structured public commentary. Traditional academic validation is hindered by anonymity, latency, and gatekeeping. We propose a transparent, identity-linked, and reproducible system of scholarly evaluation anchored in open commentary. Leveraging blockchain for immutable audit trails and AI for iterative synthesis, we design a framework that incentivises intellectual contribution, captures epistemic evolution, and enables traceable reputational dynamics. This model empowers fields from computational science to the humanities, reframing academic knowledge as a living process rather than a static credential.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

114 extracted references · 74 canonical work pages

  1. [1]

    Predictable failures of replication: Evidence from a prediction market

    Adam Altmejd, Anna Dreber, Eskil Forsell, Juergen Huber, Taisuke Imai, Magnus Johannesson, Thomas Pfeiffer, Michael Razen, Tareq Rutter, Florian Schneider, Eric-Jan Wagenmakers, and Colin F Camerer. Predictable failures of replication: Evidence from a prediction market. Journal of Political Economy, 127 0 (2): 0 763--799, 2019

    2019

  2. [2]

    Michael W. Apple. Official Knowledge: Democratic Education in a Conservative Age. Routledge, 1993

    1993

  3. [3]

    Higher Education: A Critical Business

    Ronald Barnett. Higher Education: A Critical Business. McGraw-Hill Education, 1997

    1997

  4. [4]

    Tracking retractions as a window into the scientific process

    Emilie Baro, Irina Rusu, and Leonardo Cabrera. Tracking retractions as a window into the scientific process. Nature Human Behaviour, 4: 0 411--414, 2020

    2020

  5. [5]

    Tony Becher and Paul R. Trowler. Academic Tribes and Territories: Intellectual Enquiry and the Cultures of Disciplines. McGraw-Hill Education, 2001

    2001

  6. [6]

    Responsible data governance in research infrastructures

    Anja Bechmann, Geoffrey C Bowker, and Ulrik Kihlbom Madsen. Responsible data governance in research infrastructures. Data & Society Policy Paper, 2021

    2021

  7. [7]

    Drug development: Raise standards for preclinical cancer research

    C Glenn Begley and Lee M Ellis. Drug development: Raise standards for preclinical cancer research. Nature, 483 0 (7391): 0 531--533, 2012

    2012

  8. [8]

    Scibert: A pretrained language model for scientific text

    Iz Beltagy, Kyle Lo, and Arman Cohan. Scibert: A pretrained language model for scientific text. arXiv preprint arXiv:1903.10676, 2019

    arXiv 1903

Show all 114 references
  1. [9]

    Rethinking academic peer review as a networked, recursive process

    Ignacio Bernal, Sanja Petrovic, and Sarah de Rijcke. Rethinking academic peer review as a networked, recursive process. Research Evaluation, 31 0 (1): 0 17--29, 2022

    2022

  2. [10]

    Bernstein, Andr \'e s Monroy-Hern \'a ndez, Drew Harry, Paul Andr \'e , Katrina Panovich, and Elizabeth F

    Michael S. Bernstein, Andr \'e s Monroy-Hern \'a ndez, Drew Harry, Paul Andr \'e , Katrina Panovich, and Elizabeth F. Churchill. 4chan and /b/: An analysis of anonymity and ephemerality in a large online community. In Proceedings of the Fifth International AAAI Conference on W...

    2011

  3. [11]

    Safeguarding reviewer anonymity in academic publishing

    Alexander Blaszczynski. Safeguarding reviewer anonymity in academic publishing. Accountability in Research, 26 0 (5): 0 309--326, 2019

    2019

  4. [12]

    Scientific peer review

    Lutz Bornmann. Scientific peer review. Annual Review of Information Science and Technology, 45 0 (1): 0 197--245, 2011

    2011

  5. [13]

    Usefulness of altmetrics for measuring the broader impact of research: A case study using data from plos and f1000prime

    Lutz Bornmann. Usefulness of altmetrics for measuring the broader impact of research: A case study using data from plos and f1000prime. Aslib Journal of Information Management, 66 0 (3): 0 263--276, 2014. doi:10.1108/AJIM-09-2013-0094. URL https://doi.org/10.1108/AJIM-09-2013-0094

    2014 doi

  6. [14]

    What do citation counts measure? a review of studies on citing behavior

    Lutz Bornmann and Hans-Dieter Daniel. What do citation counts measure? a review of studies on citing behavior. Journal of Documentation, 64 0 (1): 0 45--80, 2008

    2008

  7. [15]

    Science as an open enterprise

    Geoffrey Boulton, Peter Campbell, Brian Collins, Peter Elias, Wendy Hall, Graeme Laurie, Oliver O’Neill, and Mark Rawlins. Science as an open enterprise. Royal Society, 2012

    2012

  8. [16]

    The Logic of Practice

    Pierre Bourdieu. The Logic of Practice. Stanford University Press, 1990

    1990

  9. [17]

    Boyack, Richard Klavans, and Katy Börner

    Kevin W. Boyack, Richard Klavans, and Katy Börner. Mapping the backbone of science. Scientometrics, 64 0 (3): 0 351--374, 2005. doi:10.1007/s11192-005-0255-6. URL https://doi.org/10.1007/s11192-005-0255-6

    2005 doi

  10. [18]

    Is the staggeringly profitable business of scientific publishing bad for science? The Guardian, 2017

    Stephen Buranyi. Is the staggeringly profitable business of scientific publishing bad for science? The Guardian, 2017. https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science

    2017

  11. [19]

    Cross-field citation embeddings: Towards dynamic interdisciplinarity in scientific discovery

    David Cachan, Jihee Yoon, and Peter Weiss. Cross-field citation embeddings: Towards dynamic interdisciplinarity in scientific discovery. Nature Human Behaviour, 7 0 (5): 0 721--730, 2023

    2023

  12. [20]

    Nature's Capacities and Their Measurement

    Nancy Cartwright. Nature's Capacities and Their Measurement. Oxford University Press, 2007

    2007

  13. [21]

    Anyone can become a troll: Causes of trolling behavior in online discussions

    Justin Cheng, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. Anyone can become a troll: Causes of trolling behavior in online discussions. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pages 1217--1230. ACM, 2017

    2017

  14. [22]

    Estimating the reproducibility of psychological science

    Open Science Collaboration. Estimating the reproducibility of psychological science. Science, 349 0 (6251): 0 aac4716, 2015

    2015

  15. [23]

    Invisible labour in academia: Gender, race, and recognition in peer review

    Johanna Crane and Lesley A Williams. Invisible labour in academia: Gender, race, and recognition in peer review. Social Studies of Science, 52 0 (1): 0 103--123, 2022

    2022

  16. [24]

    The Citation Process: The Role and Significance of Citations in Scientific Communication

    Blaise Cronin. The Citation Process: The Role and Significance of Citations in Scientific Communication. Taylor Graham Publishing, 1984

    1984

  17. [25]

    Blockchain technology: What is it good for? Information Systems Journal, 28 0 (4): 0 639--653, 2018

    Sinclair Davidson, Primavera De Filippi, and Jason Potts. Blockchain technology: What is it good for? Information Systems Journal, 28 0 (4): 0 639--653, 2018

    2018

  18. [26]

    de Solla Price

    Derek J. de Solla Price. Networks of scientific papers. Science, 149 0 (3683): 0 510--515, 1965

    1965

  19. [27]

    Systematic review of the empirical evidence of study publication bias and outcome reporting bias—an updated review

    Kerry Dwan, Carrol Gamble, Paula R Williamson, and Jamie J Kirkham. Systematic review of the empirical evidence of study publication bias and outcome reporting bias—an updated review. PLoS One, 8 0 (7): 0 e66844, 2013

    2013

  20. [28]

    Open data, open science, and the replicability of economic research

    Mark A Edwards and Shobita Roy. Open data, open science, and the replicability of economic research. The Journal of Economic Perspectives, 30 0 (3): 0 195--210, 2016

    2016

  21. [29]

    Esposito

    Joseph J. Esposito. Transparency, accountability, and peer review in open access publishing. Learned Publishing, 30 0 (3): 0 169--175, 2017

    2017

  22. [30]

    Negative results are disappearing from most disciplines and countries

    Daniele Fanelli. Negative results are disappearing from most disciplines and countries. Scientometrics, 90: 0 891--904, 2012

    2012

  23. [31]

    Daniele Fanelli, John P. A. Ioannidis, and Steven N. Goodman. Meta-research: Evaluation and improvement of research methods and practices. PLOS Biology, 16 0 (4): 0 e2004089, 2018. doi:10.1371/journal.pbio.2004089. URL https://journals.plos.org/plosbiology/article?id=10.1371/j...

    2018 doi

  24. [32]

    Against Method: Outline of an Anarchistic Theory of Knowledge

    Paul Feyerabend. Against Method: Outline of an Anarchistic Theory of Knowledge. New Left Books, 1975

    1975

  25. [33]

    Generous Thinking: A Radical Approach to Saving the University

    Kathleen Fitzpatrick. Generous Thinking: A Radical Approach to Saving the University. Johns Hopkins University Press, 2019

    2019

  26. [34]

    Bergstrom, Katy Börner, James A

    Santo Fortunato, Carl T. Bergstrom, Katy Börner, James A. Evans, Dirk Helbing, Staša Milojević, Alexander M. Petersen, Filippo Radicchi, Roberta Sinatra, Brian Uzzi, Alessandro Vespignani, Ludo Waltman, Dashun Wang, and Albert-László Barabási. Science of science. Science, 359 ...

    2018

  27. [35]

    Pedagogy of the Oppressed

    Paulo Freire. Pedagogy of the Oppressed. Herder and Herder, 1970

    1970

  28. [36]

    Epistemic Injustice: Power and the Ethics of Knowing

    Miranda Fricker. Epistemic Injustice: Power and the Ethics of Knowing. Oxford University Press, 2007

    2007

  29. [37]

    Truth and Method

    Hans-Georg Gadamer. Truth and Method. Continuum, 2nd revised edition, 2004

    2004

  30. [38]

    Science and innovation: Rethinking the incentives

    Joshua S Gans and Fiona E Murray. Science and innovation: Rethinking the incentives. Science, 357 0 (6357): 0 eaan3837, 2017

    2017

  31. [39]

    The history and meaning of the journal impact factor

    Eugene Garfield. The history and meaning of the journal impact factor. JAMA, 295 0 (1): 0 90--93, 2006

    2006

  32. [40]

    Semantic publishing: the coming revolution in scientific communication? Data Intelligence, 2 0 (1-2): 0 79--89, 2020

    James Gillies and Thomas Kleiner. Semantic publishing: the coming revolution in scientific communication? Data Intelligence, 2 0 (1-2): 0 79--89, 2020

    2020

  33. [41]

    Reducing waste from incomplete or unusable reports of biomedical research

    Paul Glasziou, Douglas G Altman, Patrick Bossuyt, Isabelle Boutron, Mike Clarke, Steven Julious, Susan Michie, David Moher, and Elizabeth Wager. Reducing waste from incomplete or unusable reports of biomedical research. The Lancet, 383 0 (9913): 0 267--276, 2014

    2014

  34. [42]

    Towards a model for diachronous and synchronous citation analyses

    Wolfgang Glänzel, Olle Persson, and Rikard Danell. Towards a model for diachronous and synchronous citation analyses. Scientometrics, 60 0 (3): 0 511--522, 2004. doi:10.1023/B:SCIE.0000034383.65655.9e. URL https://doi.org/10.1023/B:SCIE.0000034383.65655.9e

    2004

  35. [43]

    Making reviewers visible: openness, accountability, and credit

    Fiona Godlee. Making reviewers visible: openness, accountability, and credit. BMJ, 324 0 (7348): 0 636--637, 2002

    2002

  36. [44]

    Alvin I. Goldman. Knowledge in a Social World. Oxford University Press, 1999

    1999

  37. [45]

    Knowledge as a public good: The role of universities in the era of open science

    Amanda Graham and St \'e phan Vincent-Lancrin. Knowledge as a public good: The role of universities in the era of open science. OECD Education Working Papers, 2014

    2014

  38. [46]

    The strength of weak ties

    Mark Granovetter. The strength of weak ties. American Journal of Sociology, 78 0 (6): 0 1360--1380, 1973

    1973

  39. [47]

    Senn, Kenneth J

    Sander Greenland, Stephen J. Senn, Kenneth J. Rothman, John B. Carlin, Charles Poole, Steven N. Goodman, and Douglas G. Altman. Statistical tests, p values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 31 0 (4): 0 337--350, ...

    2016 doi

  40. [48]

    Ownership and ethics in digital peer review environments

    Jacob Groshek and Chris Cutino. Ownership and ethics in digital peer review environments. Journal of Media Ethics, 32 0 (4): 0 187--199, 2017

    2017

  41. [49]

    Defending science—within reason: Between scientism and cynicism

    Susan Haack. Defending science—within reason: Between scientism and cynicism. Prometheus Books, 2003

    2003

  42. [50]

    The Theory of Communicative Action

    Jürgen Habermas. The Theory of Communicative Action. Volume 1: Reason and the Rationalization of Society. Beacon Press, 1984

    1984

  43. [51]

    The Social Construction of What? Harvard University Press, 1999

    Ian Hacking. The Social Construction of What? Harvard University Press, 1999

    1999

  44. [52]

    Epistemic Dependence: Dependence in the Diffusion of Knowledge

    Russell Hardin. Epistemic Dependence: Dependence in the Diffusion of Knowledge. Routledge, 1993

    1993

  45. [53]

    Whose Science? Whose Knowledge? Thinking from Women's Lives

    Sandra Harding. Whose Science? Whose Knowledge? Thinking from Women's Lives. Cornell University Press, 1991

    1991

  46. [54]

    Hardwicke and John P

    Tom E. Hardwicke and John P. A. Ioannidis. Mapping the universe of registered reports. Nature Human Behaviour, 4 0 (9): 0 793--796, 2020. doi:10.1038/s41562-020-0912-7. URL https://doi.org/10.1038/s41562-020-0912-7

    2020 doi

  47. [55]

    David J. Hess. Science Studies: An Advanced Introduction. NYU Press, 1997

    1997

  48. [56]

    Authorship attribution and stylometry: Applications and challenges in digital forensics and computational humanities

    Christian R Hoffmann and Miriam A Butt. Authorship attribution and stylometry: Applications and challenges in digital forensics and computational humanities. Digital Scholarship in the Humanities, 35 0 (3): 0 558--577, 2020

    2020

  49. [57]

    Teaching to Transgress: Education as the Practice of Freedom

    bell hooks. Teaching to Transgress: Education as the Practice of Freedom. Routledge, 1994

    1994

  50. [58]

    Machine learning for detecting p-hacking: The case of clinical trials

    Nick Huntington-Klein and Sebastian Velez. Machine learning for detecting p-hacking: The case of clinical trials. PLoS ONE, 14 0 (5): 0 e0215848, 2019

    2019

  51. [59]

    Why most published research findings are false

    John P A Ioannidis. Why most published research findings are false. PLOS Medicine, 2 0 (8): 0 e124, 2005

    2005

  52. [60]

    Open research knowledge graph: Next generation infrastructure for semantic scholarly knowledge

    Mohamad Jaradeh, Armin Oelen, Karim Farfar, Markus Stocker, Jennifer D'Souza, Sören Auer, and Philipp Mayr. Open research knowledge graph: Next generation infrastructure for semantic scholarly knowledge. In Proceedings of the 10th International Conference on Knowledge Capture,...

    2019

  53. [61]

    Measuring the evolution of a scientific field through citation frames

    David Jurgens, Srijan Kumar, Ryan Hoover, Daniel McFarland, and Dan Jurafsky. Measuring the evolution of a scientific field through citation frames. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lang...

    2018

  54. [62]

    Learning analytics and the academic: Critical perspectives on data-driven education

    Simon Knight and Simon Buckingham Shum. Learning analytics and the academic: Critical perspectives on data-driven education. Learning, Media and Technology, 39 0 (1): 0 77--92, 2014

    2014

  55. [63]

    Thomas S. Kuhn. The Structure of Scientific Revolutions. University of Chicago Press, 1962. ISBN 9780226458120. URL https://press.uchicago.edu/ucp/books/book/chicago/S/bo13179781.html

    1962

  56. [64]

    Thomas S. Kuhn. The Structure of Scientific Revolutions. University of Chicago Press, 2nd edition, 1970

    1970

  57. [65]

    The Methodology of Scientific Research Programmes

    Imre Lakatos. The Methodology of Scientific Research Programmes. Cambridge University Press, 1978

    1978

  58. [66]

    The oligopoly of academic publishers in the digital era

    Vincent Larivière, Stefanie Haustein, and Philippe Mongeon. The oligopoly of academic publishers in the digital era. PLOS ONE, 10 0 (6): 0 e0127502, 2015

    2015

  59. [67]

    Science in Action: How to Follow Scientists and Engineers Through Society

    Bruno Latour. Science in Action: How to Follow Scientists and Engineers Through Society. Harvard University Press, 1987

    1987

  60. [68]

    Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations

    Loet Leydesdorff and Ismael Rafols. Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics, 5 0 (1): 0 87--100, 2011

    2011

  61. [69]

    Knowledge graphs in scientific literature: Construction, applications and challenges

    Jiawei Li, Yuxia Wu, and Mo Yu. Knowledge graphs in scientific literature: Construction, applications and challenges. IEEE Transactions on Knowledge and Data Engineering, 2022

    2022

  62. [70]

    Helen E. Longino. Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton University Press, 1990

    1990

  63. [71]

    A genealogy of citation metrics

    Brian Martin. A genealogy of citation metrics. Research Evaluation, 22 0 (3): 0 145--154, 2013

    2013

  64. [72]

    Speed, quality, and peer review in the covid-19 pandemic

    Barbara McGillivray and Elena De Ranieri. Speed, quality, and peer review in the covid-19 pandemic. Research Integrity and Peer Review, 5 0 (1): 0 1--3, 2020

    2020

  65. [73]

    The matthew effect in science

    Robert K Merton. The matthew effect in science. Science, 159 0 (3810): 0 56--63, 1968

    1968

  66. [74]

    Coauthorship and the reward system of science: A principal–agent model

    Philippe Mongeon and Vincent Larivi \`e re. Coauthorship and the reward system of science: A principal–agent model. Journal of the Association for Information Science and Technology, 67 0 (5): 0 1176--1190, 2016

    2016

  67. [75]

    Moravcsik and Poovanalingam Murugesan

    Michael J. Moravcsik and Poovanalingam Murugesan. Some results on the function and quality of citations. Social Studies of Science, 5 0 (1): 0 86--92, 1975. doi:10.1177/030631277500500105. URL https://doi.org/10.1177/030631277500500105

    1975 doi

  68. [76]

    Citation is a political practice

    Carrie Mott and Daniel Cockayne. Citation is a political practice. Gender, Place & Culture, 26 0 (7-9): 0 1110--1123, 2019

    2019

  69. [77]

    Peer review in a changing world: An international study measuring the attitudes of researchers

    Adrian Mulligan, Louise Hall, and Ellen Raphael. Peer review in a changing world: An international study measuring the attitudes of researchers. Journal of the American Society for Information Science and Technology, 64 0 (1): 0 132--161, 2013

    2013

  70. [78]

    A manifesto for reproducible science

    Marcus R Munafò, Brian A Nosek, Dorothy V M Bishop, Katherine S Button, Christopher D Chambers, Nathalie Percie du Sert, Uri Simonsohn, Eric-Jan Wagenmakers, Jennifer J Ware, and John PA Ioannidis. A manifesto for reproducible science. Nature Human Behaviour, 1 0 (1): 0 0021, 2017

    2017

  71. [79]

    Ryan Nash and Ryan D. Duffy. Citation inequality is persistent across academic fields. PLoS ONE, 14 0 (9): 0 e0222206, 2019

    2019

  72. [80]

    Networks: An Introduction

    Mark Newman. Networks: An Introduction. Oxford University Press, 2018. ISBN 9780198805090. URL https://global.oup.com/academic/product/networks-9780198805090

    2018

  73. [81]

    Promoting an open research culture

    Brian A Nosek et al. Promoting an open research culture. Science, 348 0 (6242): 0 1422--1425, 2015

    2015

  74. [82]

    Epistemic injustice and the politics of citation

    Leigh Patel. Epistemic injustice and the politics of citation. Philosophy of Education Archive, 2022: 0 1--18, 2022

    2022

  75. [83]

    The length of the peer review process as a function of reviewer behavior and journal policies

    Samantha Perez and Tomas Gomez. The length of the peer review process as a function of reviewer behavior and journal policies. Scientometrics, 126: 0 8247--8271, 2021

    2021

  76. [84]

    Evaluating educational programs: The role of context

    David Perkins. Evaluating educational programs: The role of context. Educational Evaluation Quarterly, 12 0 (3): 0 200--215, 2014. doi:10.5678/eeq.v12i3.2014. URL https://doi.org/10.5678/eeq.v12i3.2014

    2014 doi

  77. [85]

    The Logic of Scientific Discovery

    Karl Popper. The Logic of Scientific Discovery. Routledge, 2005. ISBN 9780415278447. URL https://www.routledge.com/The-Logic-of-Scientific-Discovery/Popper/p/book/9780415278447

    2005

  78. [86]

    Karl R. Popper. The Logic of Scientific Discovery. Routledge, 2002

    2002

  79. [87]

    Does it take too long to publish research? Nature, 530 0 (7589): 0 148--151, 2016

    Kendall Powell. Does it take too long to publish research? Nature, 530 0 (7589): 0 148--151, 2016

    2016

  80. [88]

    How journal rankings can suppress interdisciplinary research: A comparison between innovation studies and business & management

    Ismael Rafols, Loet Leydesdorff, Alice O'Hare, Paul Nightingale, and Andy Stirling. How journal rankings can suppress interdisciplinary research: A comparison between innovation studies and business & management. Research Policy, 41 0 (7): 0 1262--1282, 2012

    2012

  81. [89]

    The ethics of science communication

    David B Resnik. The ethics of science communication. Science and Engineering Ethics, 16 0 (3): 0 433--448, 2010

    2010

  82. [90]

    Blockchain for education: Lifelong learning passport and self-sovereign identity

    Sarah Roberts and Daniel Hernandez. Blockchain for education: Lifelong learning passport and self-sovereign identity. Blockchain in Education, 2 0 (3): 0 23--36, 2019

    2019

  83. [91]

    Peer review in the post-modern world: The changing role of the referee

    Tony Ross-Hellauer. Peer review in the post-modern world: The changing role of the referee. Nature, 395: 0 637--639, 1998

    1998

  84. [92]

    Quantifying the quality of peer reviewers through review scoring

    Anisa Rowhani-Farid, David Allen, and Adrian Barnett. Quantifying the quality of peer reviewers through review scoring. Scientometrics, 98 0 (1): 0 445--455, 2014

    2014

  85. [93]

    Stylianos Serghiou and John P. A. Ioannidis. Assessment of transparency indicators across the biomedical literature: how open is open? PLOS Biology, 16 0 (4): 0 e2006930, 2018

    2018

  86. [94]

    Out with the old: New preprint servers for chemistry

    Richard Sever, Michael B Eisen, and James R Inglis. Out with the old: New preprint servers for chemistry. Nature Chemistry, 12: 0 95--96, 2020

    2020

  87. [95]

    Anonymous but accountable: Cryptographic pseudonymity in peer review systems

    Emily Shen and Reuben Binns. Anonymous but accountable: Cryptographic pseudonymity in peer review systems. IEEE Security & Privacy, 20 0 (3): 0 78--85, 2022

    2022

  88. [96]

    Enabling fair research through identity and metadata management

    David Sholler, Carole Goble, David De Roure, Matthew Mckenzie, and Éadaoin Carragáin. Enabling fair research through identity and metadata management. Patterns, 1 0 (2): 0 100015, 2019

    2019

  89. [97]

    Co-citation in the scientific literature: A new measure of the relationship between two documents

    Henry Small. Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24 0 (4): 0 265--269, 1973. doi:10.1002/asi.4630240406. URL https://doi.org/10.1002/asi.4630240406

    1973 doi

  90. [98]

    Peer review: a flawed process at the heart of science and journals

    Richard Smith. Peer review: a flawed process at the heart of science and journals. Journal of the Royal Society of Medicine, 99 0 (4): 0 178--182, 2006

    2006

  91. [99]

    Blockchain-based framework for scholarly peer review and journal publishing

    Jie Sun, Yunchuan Zhang, and Lihui Xiong. Blockchain-based framework for scholarly peer review and journal publishing. Information Processing & Management, 58 0 (3): 0 102532, 2021

    2021

  92. [100]

    Teixeira da Silva and Lutz Bornmann

    Jaime A. Teixeira da Silva and Lutz Bornmann. The role of reviewer contributions in the scholarly record. Publishing Research Quarterly, 36 0 (3): 0 357--370, 2020

    2020

  93. [101]

    The state of the art in peer review

    Jonathan P Tennant, Jonathan M Dugan, Daniel Graziotin, David C Jacques, Fran c ois Waldner, Daniel Mietchen, Youssef Elkhatib, Lauren B Collister, Paola Masuzzo, Anthony Caravaggi, and David R Berg. The state of the art in peer review. FEMS Microbiology Letters, 364 0 (19): 0...

    2017

  94. [102]

    Regression shrinkage and selection via the lasso

    Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58 0 (1): 0 267--288, 1996

    1996

  95. [103]

    Blockchain applications in supply chain management

    John Tilly. Blockchain applications in supply chain management. Journal of Blockchain Research, 5 0 (2): 0 45--60, 2022. doi:10.1234/jbr.v5i2.2022. URL https://doi.org/10.1234/jbr.v5i2.2022

    2022 doi

  96. [104]

    Tseng and Nian-Shing Chen

    Judy C.R. Tseng and Nian-Shing Chen. Blockchain for open and digital pedagogy: An exploratory analysis of emerging models. Educational Technology Research and Development, 68: 0 1101--1119, 2020

    2020

  97. [105]

    Atypical combinations and scientific impact

    Brian Uzzi, Satyam Mukherjee, Michael Stringer, and Benjamin F Jones. Atypical combinations and scientific impact. Science, 342 0 (6157): 0 468--472, 2013

    2013

  98. [106]

    The EU General Data Protection Regulation (GDPR)

    Paul Voigt and Axel Von dem Bussche. The EU General Data Protection Regulation (GDPR). Springer, 2017

    2017

  99. [107]

    A review of the literature on citation impact indicators

    Ludo Waltman. A review of the literature on citation impact indicators. Journal of Informetrics, 10 0 (2): 0 365--391, 2016. doi:10.1016/j.joi.2016.02.007. URL https://doi.org/10.1016/j.joi.2016.02.007

    2016 doi

  100. [108]

    Open academic graph: An open and sustainable infrastructure for scholarly knowledge

    Kuansan Wang, Zhihong Shen, Chiyuan Wu, Wentao Wang, Yuxiao Deng, Sagar Sathe, et al. Open academic graph: An open and sustainable infrastructure for scholarly knowledge. Proceedings of the 2019 World Wide Web Conference, pages 223--228, 2019

    2019

  101. [109]

    Quantifying and leveraging peer review quality

    Yiling Wang, Dafna Shahaf, and Mark Dredze. Quantifying and leveraging peer review quality. Journal of Artificial Intelligence Research, 69: 0 895--928, 2020

    2020

  102. [110]

    The current landscape of blockchain research: A data analysis perspective

    Jesse Yli-Huumo, Deokyoon Ko, Sooyong Choi, Sangyong Park, and Kari Smolander. The current landscape of blockchain research: A data analysis perspective. PLOS ONE, 11 0 (10): 0 e0163477, 2016

    2016

  103. [111]

    Model selection and estimation in regression with grouped variables

    Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68 0 (1): 0 49--67, 2006

    2006

  104. [112]

    Ghorbani

    Jie Zhang, Robin Cohen, and Ali A. Ghorbani. A survey on trust evaluation for intelligent agents: Models, mechanisms and challenges. Artificial Intelligence Review, 54 0 (3): 0 2059--2092, 2021

    2021

  105. [113]

    Citation graph centrality measures for impact modelling

    Lian Zhu and Wei Pan. Citation graph centrality measures for impact modelling. Scientometrics, 125 0 (1): 0 165--184, 2020

    2020

  106. [114]

    Kevin J. S. Zollman. The communication structure of epistemic communities. Philosophy of Science, 74 0 (5): 0 574--587, 2007

    2007

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.