E-Journal Metrics for Collection Management: Exploring Disciplinary Usage Differences in Scopus and Web of Science
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Objective – The purpose was to determine whether a relationship exists between journal downloads and either faculty authoring venue or citations to these faculty, or whether a relationship exists between journal rankings and local authoring venues or citations. A related purpose was to determine if any such relationship varied between or within disciplines. A final purpose was to determine if specific tools for ranking journals or indexing authorship and citation were demonstrably better than alternatives. Methods – Multiple years of journal usage, ranking, and citation data for twelve disciplines were combined in Excel, and the strength of relationships were determined using rank correlation coefficients. Results – The results illustrated marked disciplinary variation as to the degree that faculty decisions to download a journal article can be used as a proxy to predict which journals they will publish in or which journals will cite faculty’s work. While journal access requests show moderate to strong relationships with the journals in which faculty publish, as well as journals whose articles cite local faculty, the data suggest that Scopus may be the better resource to find such information for these journals in the health sciences and Web of Science may be the better resource for all other disciplines analyzed. The same can be said for the ability of external ranking mechanisms to predict faculty publishing behaviours. Eigenfactor is more predictive for both authoring and citing-by-others across most of the representative disciplines in the social sciences as well as the physical and natural sciences. With the health sciences, no clear pattern emerges. Conclusion – Collecting and correlating authorship and citation data allows patterns of use to emerge, resulting in a more accurate picture of use activity than the commonly used cost-per-use method. To find the best information on authoring activity by local faculty for subscribed journals, use Scopus. To find the best information on citing activity by faculty peers for subscribed titles use Thomson Reuters’ customized Local Journal Use Reports (LJUR), or limit a Web of Science search to local institution. The Eigenfactor and SNIP journal quality metrics results can better inform selection decisions, and are publicly available. Given the trend toward more centralized collection development, it is still critical to obtain liaison input no matter what datasets are used for decision making. This evidence of value can be used to defend any local library “tax” that academic departments pay as well as promote services to help faculty demonstrate their research impact.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,036 | 0,069 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,245 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 découle