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Enregistrement W3084783588 · doi:10.1016/s2214-109x(20)30416-2

Elements of trust in peer review (and our annual thanks)

2020· article· en· W3084783588 sur OpenAlexaboutno aff
Zoë Mullan

Notice bibliographique

RevueThe Lancet Global Health · 2020
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueAcademic Publishing and Open Access
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScopusScrutinyPopularityPeer reviewNegotiationMedical journalMedicineInternet privacyLibrary sciencePublic relationsPolitical sciencePsychologyMEDLINEComputer scienceLaw

Résumé

récupéré en direct d'OpenAlex

This year the traditional academic peer review model has come under scrutiny like never before. Does it hinder the rapid dissemination of information vital to clinical and public health decision making? Can it be manipulated? Does it fail to prevent publication of flawed or fraudulent work? It would be difficult to answer any of these questions in the negative, so can we really trust peer review to identify the most relevant and reliable scientific research? This is the question posed for Peer Review Week 2020, marked during Sept 21–25. Preprints, characterised by their publication ahead of peer review, have reached new levels of popularity and prominence this year, and The Lancet Group has recently committed to continuing with its own platform, Preprints with The Lancet, after an initial pilot was received positively.1Kleinert S Horton R on behalf of the Editors of the Lancet GroupPreprints with The Lancet are here to stay.Lancet. 2020; 396: 805Summary Full Text Full Text PDF Scopus (9) Google Scholar But as we wrote in an Editorial earlier this year,2The Lancet Global HealthPublishing in the time of COVID-19.Lancet Glob Health. 2020; 8: e860Summary Full Text Full Text PDF PubMed Scopus (15) Google Scholar despite their advantages in terms of rapid sharing of the potential direction of the answers to time-sensitive research questions, the sheer volume and variability in quality of preprints across a single platform makes it almost impossible for a user to negotiate them meaningfully. Self-serving as it sounds, there is still no proven superior to the tried-and-tested formula of journal-based peer review for assessing the quality of a piece of scientific research—ie, independently chosen external peer reviewers submitting formal critiques, anonymously or not, followed by editor-led decision-making over acceptance or rejection. So what of the problems with slowness, manipulation, and fallibility? The answer, I suggest, lies in the quality of the editorial oversight. As regards timeliness, reviewers could justifiably be forgiven for declining, or deprioritising, invitations to review from a journal that has previously sent very low-quality work. Such work should have been screened out by the editor. Manipulation can be minimised by a keen editorial eye for conflicts of interest and independent verification of reviewers' identity. Flawed and fraudulent work is likely to slip through even the tightest of nets on occasion, particularly when peer review is done at great speed, but the important point here is to have robust editorial mechanisms in place to rectify such issues, resulting in timely correction or retraction. Having a truly diverse and representative reviewer pool is, I suggest, a good way to enhance trust in the peer review process. As part of The Lancet family's ongoing commitments to inclusion and diversity, we analysed our reviewers over the past 12 months by gender and country of origin. Of the 720 individuals who provided at least one review and whose gender we could identify, 288 (40%) were female and 432 (60%) were male. This represents another small improvement in gender balance year-on-year since the 36%/64% split we saw when we first started analysing gender in 2018. In terms of geographic diversity, however, although our reviewers were from 72 different countries overall, almost half were from either the USA or UK, as they were in 2018. The next highest were India on 5%; Australia, China, and Switzerland on 4%; and South Africa and Canada on 3% (appendix pp 1–2). We would expect a degree of skewing, since our choice of reviewers largely reflects research output, and some countries clearly dominate over others in this area. However, the low proportion of Chinese reviewers compared with China's status as the world's largest producer of scientific publications3National Science FoundationPublications output: US trends and international comparisons. National Science Foundation, Alexandria2019https://ncses.nsf.gov/pubs/nsb20206/Date accessed: September 8, 2020Google Scholar is a clear anomaly which we will be exploring over the coming year. Additionally, although we currently make focused efforts to include at least one reviewer from the country or region where a study was done, we believe this is not sufficient. Going forward, we will therefore aim to invite a majority of reviewers from the country or region of interest among our subject specialists. Broadening the geographical diversity of our statistical reviewers is a further area of work for us over the next 12 months. Every Peer Review Week, we publicly name and thank all those who have reviewed for us over the past 12 months (appendix p 3). This year we owe a particularly large debt of gratitude to the reviewers who spared their limited time to review for us, sometimes in a matter of 3 days, during the unprecedented events of early 2020. We know full well that some had been drafted in to perform pandemic-related clinical duties in addition to their own research, had family who became ill, or had small children at home. Reviewers, your dedication to the advancement of science and medicine through helping us to identify the most robust and impactful work has been inspirational and we sincerely thank you. I thank Francesca Cullura for data analysis and figure production. I declare no competing interests. Download .pdf (.7 MB) Help with pdf files Supplementary appendix

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,825
Score d'incertitude au seuil0,563

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,193
Tête enseignante GPT0,509
Écart entre enseignants0,315 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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