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Enregistrement W6910188788 · doi:10.48448/zvkv-zn07

Prevalence of Honorary Authorship According to Different Authorship Recommendations and Contributor Role Taxonomy (CRediT) Statements | VIDEO

2022· other· en· W6910188788 sur OpenAlexaboutno aff

Notice bibliographique

RevueUnderline Science Inc. · 2022
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTaxonomy (biology)AccountabilityPublishingMEDLINEConflict of interestSet (abstract data type)

Résumé

récupéré en direct d'OpenAlex

Objective The International Committee of Medical Journal Editors (ICMJE) provided a set of minimum criteria for authorship.1 These recommendations have been adapted for all sciences in an article by McNutt and colleagues.2 The main difference between these 2 sets of recommendations is that the science-wide recommendations do not require authors to draft or revise the work.1,2 This study aimed to identify the proportion of authors who, based on their self-compiled Contributor Role Taxonomy (CRediT) statements, did not meet the minimum criteria for authorship (ie, were honorary authors) according to these 2 sets of recommendations. Furthermore, the study aimed to identify the proportion of authors who supplied only resources and/or funding to a study. Such authors were identified as “supply authors,” and this practice is considered to be a subtype of honorary authorship.<br> <br>Design Cross-sectional study of CRediT statements published in scholarly articles. The Public Library of Science (PLOS) provided CRediT statements and associated data for authors of articles published in PLOS journals between July 2017 and October 2021. Two investigators independently evaluated the authorship recommendations1,2 and developed logical operations of CRediT items for each recommendation. A third investigator acted as an arbiter in case of disagreement. The criteria related to approval and accountability for both recommendations could not be verified because of current CRediT items. For the second objective of the study, authors who contributed only to roles funding acquisition and/or resources were identified.<br> <br>Results A total of 629,046 CRediT statements (1 per author) originating from 82,683 journal articles were included. Of the CRediT statements, 34.8% (n = 218,563; 95% CI, 34.7%34.9%) indicated that the contributions provided by the author were not sufficient to qualify for authorship according to the ICMJE recommendations. Based on science-wide recommendations, 3.6% (n = 22,575; 95% CI, 3.5%-3.6%) of the authors did not qualify for authorship. Sensitivity analyses accounting for potentially ambiguous CRediT items provided similar results. The odds of fulfilling only 1 of the recommendations steadily decreased from 2017 to 2021 (Table 1), and authors of articles published in nonmedical journals had 1.11 times the odds to fulfill only 1 of the recommendations compared with authors of articles published in medical journals. Overall, 8394 authors (1.33%; 95% CI, 1.31%-1.36%) were “supply authors.” Their prevalence decreased in the years from 2017 to 2019 (difference in proportion, 0.6%; 95% CI, 0.57%-0.63%) but remained unchanged from 2019 to 2021 (0%; 95% CI, −0.02% to 0.02%).<br> <br>Conclusions Based on self-compiled CRediT statements, honorary authorship is still prevalent in science, although it seems to have steadily decreased in recent years. A seemingly minor edit applied to the ICMJE recommendations resulted in substantially different authorship requirements. Efforts should be directed toward developing consensus on core tasks to qualify for authorship that are widely applicable in science. Additional strategies should be implemented to address “supply authorship.”<br> <br>Table 1. Prevalence of Honorary Authorship Based on CRediT Statements Depending on 2 Authorship Recommendations and the Association of Year and Journal Area With Disagreements Between Recommendations<br> <br>https://assets.underline.io/uploads/markdown_image/1/image/e020149a07644589bc36ea5518d9e003.png https://assets.underline.io/uploads/markdown_image/1/image/b2f5d13c1f1a80d2be85c7493c4f9bd0.png References 1. International Committee of Medical Journal Editors (ICMJE). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. Updated December 2021. Accessed February 27, 2022. http://www.icmje.org/icmje-recommendations.pdf 2. McNutt MK, Bradford M, Drazen JM, et al. Transparency in authors’ contributions and responsibilities to promote integrity in scientific publication. Proc Natl Acad Sci U S A. 2018;115(11):2557-2560. doi:10.1073/pnas.1715374115 1College of Veterinary Medicine, Cornell University, Ithaca, NY, USA, nicoladiggi@gmail.com; 2Journal of Small Animal Practice, British Small Animal Veterinary Association, Gloucestershire, UK; 3Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands; 4Private practice of orthodontics, Milan, Italy; 5École de bibliothéconomie et des sciences de l’information, Université de Montréal, Montréal, QC, Canada; 6School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA<br> <br>Conflict of Interest Disclosures Nicola Di Girolamo is an editor in chief of 2 peer-reviewed journals, 1 published by Elsevier and 1 by Wiley. No other disclosures were reported.<br> <br>Additional Information The protocol of this research study was registered in the Open Science Framework (https://osf.io/ezpxs/). https://assets.underline.io/uploads/markdown_image/1/image/3b7cc05bc88eb604a84a4c5bbd38244a.png

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)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,555
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0020,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

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 Opus0,058
Tête enseignante GPT0,346
Écart entre enseignants0,287 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

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
Publié2022
Routes d'admission1
Résumé présentoui

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