Open Access Works are as Reliable as Other Publishing Models at Retracting Flawed Articles from the Biomedical Literature
Notice bibliographique
Résumé
A Review of: Peterson, G.M. (2013). Characteristics of retracted open access biomedical literature: a bibliographic analysis. Journal of the American Society for Information Science and Technology. 64(12), 2428-2436. doi: 10.1002/asi.22944 Abstract Objective – To investigate whether the rate of retracted articles and citation rates post-retraction in the biomedical literature are comparable across open access, free-to-access, or pay-to-access journals. Design – Citation analysis. Setting – Biomedical literature. Subjects – 160 retracted papers published between 1st January 2001 and 31st December 2010. Methods – For the retracted papers, 100 records were retrieved from the PubMed database and 100 records from the PubMed Central (PMC) open access subset. Records were selected at random, based on the PubMed identifier. Each article was assigned a number based on its accessibility using the specific criteria. Articles published in the PMC open access subset were assigned a 2; articles retrieved from PubMed that were freely accessible, but did not meet the criteria for open access were assigned a 1; and articles retrieved through PubMed which were pay-to-access were assigned a 0. This allowed articles to be grouped and compared by accessibility. Citation information was collected primarily from the Science Citation Index. Articles for which no citation information was available, and those with a lifetime citation of 0 (or 1 where the citation came from the retraction statement) were excluded, leaving 160 articles for analysis. Information on the impact factor of the journals was retrieved and the analysis was performed twice; first with the entire set, and second after excluding articles published in journals with an impact factor of 10 or above (14% of the total). The average number of citations per month was used to compare citation rates, and the percentage change in citation rate pre- and post-retraction was calculated. Information was also collected on the time between the date the original article was published and the date of retraction, and the availability of information on the reason for the retraction. Main results – The overall rate of retracted articles in the PMC open access subset compared with the wider PubMed dataset was similar (0.049% and 0.028% respectively). In the group with an accessibility rating of 0, the change in citation rate pre- and post-retraction was -41%. For the group with an accessibility rating of 1, the change was -47% and in those with a rating of 2, the change in citation rate was -59%. Removing articles published in high impact factor journals did not change the results significantly. Retractions were issued more slowly for free access papers compared with open or fee-based articles. The bibliographic records for open access articles disclosed details of the reason for the retraction more frequently than free, non-open papers (91% compared to 53%). Conclusion – Open access literature is similar in its rate of retraction and the reduction in post-retraction citations to the rest of the biomedical literature, and is actually more reliable at reporting the reason for the retraction.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,330 | 0,759 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,068 | 0,066 |
| Études des sciences et des technologies | 0,005 | 0,010 |
| Communication savante | 0,044 | 0,049 |
| Science ouverte | 0,006 | 0,016 |
| Intégrité de la recherche | 0,009 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,033 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».