Correction: Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact
Notice bibliographique
Résumé
minor error in the references section in the originally published version of the editorial by Eysenbach (J Med Internet Res 2011;13[4]:e123) on the relationship between citations and tweetations has been corrected; in addition, references being part of the dataset are no longer cited as “references”. The now corrected problem with the references was a formatting problem only and had no impact on the study findings. The originally published article stated correctly that all 55 articles published between issue 3/2009 and 2/2010 were included, but the cited references erroneously contained 12 additional references from issue 2/2009, which were not part of the analysis, for the reasons described in the article (sparse tweetations pre-issue 3/2009). In the corrected version we have not only removed these extra 12 references, but we also took the opportunity to move all other references of included articles into a new Multimedia Appendix 2, no longer citing them in the “References” section. We now refer to them in the paper by article ID (last 4-digits of the DOI), where we previously used in-text citations (Table 2 and Discussion). The original decision to cite them as references was made for the sake of convenience for our readers, to prevent them from having to look up the references in a separate file or by DOI. JMIR has no space limitations and prefers to cite references in the article rather than in an Appendix; for readers downloading a PDF file it is more convenient to have all references in a single file rather than having to download a separate Appendix. The decision to now move these references into a Multimedia Appendix was made after a reader pointed out that citing these articles may increase JMIR’s impact factor. Although none of the two peer-reviewers, both experts in scientometrics, were originally concerned about citing the included articles as references, and even though any potential additional impact factor points after the decimal point caused by the original editorial would have been neglible (after all, these articles are already highly cited: altogether, 638 times, according to Google Scholar), we wish to avoid any potential debate or uncertainty on what proportion of future JMIR impact factors were caused by this editorial, and have therefore decided to pre-emptively move these references into a separate file (Multimedia Appendix 2). The article correction was made on January 4, 2012, before submission to PubMed Central, Swets and other content aggregators and databases, and before indexing by Thomson Reuters. Having to remove references from a manuscript to preserve the validity of a journal-level impact metric is somewhat troubling, but if anything then this perhaps illustrates the limitations and tyranny of the impact factor, and why we should consider additional metrics.
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 | 0,019 | 0,393 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,059 | 0,040 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».