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Enregistrement W2040635533 · doi:10.1177/0883073810365011

Academic Merit, Promotion, and Journal Peer Reviewing: The Role of Academic Institutions In Providing Proper Recognition

2010· editorial· en· W2040635533 sur OpenAlexaff
Lorraine E. Ferris, Roger A. Brumback

Notice bibliographique

RevueJournal of Child Neurology · 2010
Typeeditorial
Langueen
DomaineDecision Sciences
ThématiqueMeta-analysis and systematic reviews
Établissements canadiensUniversity of TorontoPublic Health Ontario
Organismes subventionnairesnon disponible
Mots-clésPromotion (chess)PsychologyPeer reviewMedical educationMedicinePolitical scienceLaw

Résumé

récupéré en direct d'OpenAlex

The recent Sixth International Congress on Peer Review and Biomedical Publication (September 2009) was packed with intense discussions about improving quality and credibility of biomedical publishing. Meeting every 4 years, the Congress has provided the opportunity for editors (past and present) to confer and debate on what constitutes quality publishing and how to achieve and maintain it. Not surprisingly, external peer review was one of the key topics: (1) What makes a good peer reviewer? (2) How does the quality of reviews submitted by reviewers differ over their careers? and (3) How can we train, encourage, and attract the best quality peer reviewers? Although the use of external peer reviewers is now seen as the fundamental approach for improving quality in science, it is easy to forget that many scientific journals did not use peer review until the 20th century. Unfortunately, peer review is an imperfect system, and a 2003 Cochrane Review found little empirical evidence to support editorial peer review as a means to improve biomedical publishing. Nonetheless, that Cochrane Review identified methodological problems in research on peer review and stated ‘‘that the absence of evidence on efficacy and effectiveness cannot be interpreted as evidence of their absence.’’ However, high-quality peer review does seem to have value in advancing biomedical publishing and our suggestion of the characteristics of that ideal peer reviewer (to provide this high-quality peer review) is shown in Table 1. In essence, the high-quality peer reviewer is a colleague who makes a major contribution to the quality of the scientific literature by having been involved in the peer-review process. The importance of this contribution cannot be understated and that gets to the core of assuring adequate recognition of these colleagues for their valuable contributions. Authors making contributions to the biomedical literature automatically have something substantive to show for their time and expertise (author names appear under the title of the article and are listed by the major indexing services, such as Medline [PubMed]). In contrast, peer reviewers, by and large, are anonymous with no tangible evidence of their contributions. This lack of tangible recognition impairs the ability of journal editors to entice reviewers. This is particularly problematic in the biomedical sciences where productivity measures are now increasingly tied to salary, rank, and tenure. Likely there are some internal motivations to be a peer reviewer including helping to advance their field, staying up to date, and educating oneself by learning what other reviewers reading the same paper thought of it (assuming editors share the reviews), which helps in getting people to agree to review, but it does not recognize the contributions to the biomedical literature. However, volunteering time to provide peer review that is not recognized as scholarly achievement and thus would reduce time devoted to other measurable productivity standards will generally be discouraged in academic environments. This makes it difficult for journal editors to obtain peer reviews of submitted materials. For example, for the Journal of Child Neurology, as the number of noneditorial submissions has increased from approximately 270 in 2005 to more than 370 in 2009, the ability to obtain more than 1 completed peer review per submission has decreased over that same time period from nearly 25% to less than 5%. This highlights a crisis that threatens to undermine the quality of biomedical publications and the urgency of finding a solution that will permit peer reviewing to be considered by the scientific community as a recognized valuable scholarly contribution. How can journals acknowledge high-quality peer reviewers in a way that will be tangible as scholarly achievement that the academic community will be willing to recognize? Some of the current mechanisms used to recognize the contributions of peer reviewers include publishing reviewer names periodically, providing continued medical education (CME) credits, offering discounts for journal subscriptions, or providing

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,286
score de la tête « metaresearch » (Gemma)0,562
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,714
Score d'incertitude au seuil0,880

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2860,562
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0040,001
Bibliométrie0,0110,011
Études des sciences et des technologies0,0140,036
Communication savante0,0610,035
Science ouverte0,0050,018
Intégrité de la recherche0,0190,021
Charge utile insuffisante (le modèle a refusé de juger)0,0070,008

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 Opus0,461
Tête enseignante GPT0,477
Écart entre enseignants0,016 · 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
DomaineIncitatifs
GenreÉditorial

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

En bref

Citations12
Publié2010
Routes d'admission1
Résumé présentoui

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