The Use of Bibliometrics in the Social Sciences and Humanities
Notice bibliographique
Résumé
The Social Sciences and Humanities Research Council (SSHRC) asked Science-Metrix to identify current practices in bibliometric evaluation of research in the social sciences and humanities (SSH). The resulting study involves a critical review of the literature in order to identify the specific characteristics of the SSH and their effects on the use of bibliometrics for evaluating and mapping research. In addition, this report presents an overview of methods of research benchmarking and mapping and identification of emerging SSH fields. This part of the report is particularly relevant because of the need to exercise considerable caution when using bibliometrics to evaluate and map SSH research. This report shows that bibliometrics must be used with care and caution in a number of SSH disciplines. Knowledge dissemination media in the SSH are different from those in the natural sciences and engineering (NSE), particularly because of the much greater role of books in the SSH. Articles account for 45% to 70% of research output in the social sciences and for 20% to 35% in the humanities, depending on the discipline. Bibliometric analyses that focus solely on research published in journals may not give an accurate representation of SSH research output. In addition, bibliometric analyses reflect the biases of the databases used. For example, the Social Science Citation Index (SSCI) and the Arts and Humanities Citation Index (AHCI) of Thomson ISI over-represent research output published in English. Original findings produced by this study show that the bias results in an estimated 20-25% over-representation of English material in the two databases. Findings from the scientific literature support those of Science-Metrix. In order to benchmark national performances and identify Canada's strengths in SSH, it is possible to use research articles published in journals representing disciplines where this medium of communication is popular, such as economics. For other disciplines, journal-based bibliometric analysis may be used with due caution and databases can be built in order to factor in other knowledge dissemination media. However, one must be wary of conducting comparative analyses of SSH disciplines without taking into account the effects of the knowledge dissemination media of each discipline on the bibliometric tools being used. Bibliometric methods have not yet been refined to the point where they can serve to identify emerging fields. In this regard, the methods with the greatest potential are co-citation analysis, co-word analysis and bibliographic coupling. However, their usefulness for policy development has been challenged. It is therefore preferable to combine bibliometrics with research monitoring and even peer review for identifying emerging fields. Another approach is to track the development of bibliometric methods, which nonetheless show promise on many fronts. In short, bibliometrics must be used carefully for SSH research evaluation. Furthermore, each discipline has its own specific characteristics, so bibliometrics is to be applied differently in each case. This report presents original findings that will help in determining how bibliometric analysis should be applied to the various SSH disciplines. It is possible to adopt at least two possible attitudes toward the challenge of offsetting the limitations of bibliometrics: a passive one (laissez faire) or a proactive one (interventionism). Given current trends such as the increased publication of articles and open access, the laissez faire approach may be the most effective way of enhancing the validity of SSH bibliometric analysis. The interventionist approach focuses on creating and optimizing databases such as the Common CV System.
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 | 0,289 | 0,560 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,003 |
| Bibliométrie | 0,158 | 0,221 |
| Études des sciences et des technologies | 0,007 | 0,012 |
| Communication savante | 0,026 | 0,021 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».