Personal Publications Lists Serve as a Reliable Calibration Parameter to Compare Coverage in Academic Citation Databases with Scientific Social Media
Notice bibliographique
Résumé
A Review of: Hilbert, F., Barth, J., Gremm, J., Gros, D., Haiter, J., Henkel, M., Reinhardt, W., & Stock, W.G. (2015). Coverage of academic citation databases compared with coverage of scientific social media: personal publication lists as calibration parameters. Online Information Review 39(2): 255-264. http://dx.doi.org/10.1108/OIR-07-2014-0159 Abstract Objective – The purpose of this study was to explore coverage rates of information science publications in academic citation databases and scientific social media using a new method of personal publication lists as a calibration parameter. The research questions were: How many publications are covered in different databases, which has the best coverage, and what institutions are represented and how does the language of the publication play a role? Design – Bibliometric analysis. Setting – Academic citation databases (Web of Science, Scopus, Google Scholar) and scientific social media (Mendeley, CiteULike, Bibsonomy). Subjects – 1,017 library and information science publications produced by 76 information scientists at 5 German-speaking universities in Germany and Austria. Methods – Only documents which were published between 1 January 2003 and 31 December 2012 were included. In that time the 76 information scientists had produced 1,017 documents. The information scientists confirmed that their publication lists were complete and these served as the calibration parameter for the study. The citations from the publication lists were searched in three academic databases: Google Scholar, Web of Science (WoS), and Scopus; as well as three social media citation sites: Mendeley, CiteULike, and BibSonomy and the results were compared. The publications were searched for by author name and words from the title. Main results – None of the databases investigated had 100% coverage. In the academic databases, Google Scholar had the highest amount of coverage with an average of 63%, Scopus an average of 31%, and lowest was WoS with an average of 15%. On social media sites, Bibsonomy had the highest coverage with an average of 24%, Mendeley had an average coverage of 19%, and the lowest coverage was CiteULike with an average of 8%. Conclusion – The use of personal publication lists are reliable calibration parameters to compare coverage of information scientists in academic citation databases with scientific social media. Academic citation databases had a higher coverage of publications, in particular, Google Scholar, compared to scientific social media sites. The authors recommend that information scientists personally publish work on social media citation databases to increase exposure. Formulating a publication strategy may be useful to identify journals with the most exposure in academic citation databases. Individuals should be encouraged to keep personal publication lists and these can be used as calibration parameters as a measure of coverage in the future.
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 | 0,088 | 0,411 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,038 | 0,064 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,008 | 0,012 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
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 ».