Metadata Quality in Institutional Repositories May be Improved by Addressing Staffing Issues
Notice bibliographique
Résumé
A Review of: Moulaison Sandy, H., & Dykas, F. (2016). High-quality metadata and repository staffing: Perceptions of United States–based OpenDOAR participants. Cataloging & Classification Quarterly, 54(2), 101-116. http://dx.doi.org/10.1080/01639374.2015.1116480 Objective – To investigate the quality of institutional repository metadata, metadata practices, and identify barriers to quality. Design – Survey questionnaire. Setting – The OpenDOAR online registry of worldwide repositories. Subjects – A random sample of 50 from 358 administrators of institutional repositories in the United States of America listed in the OpenDOAR registry. Methods – The authors surveyed a random sample of administrators of American institutional repositories included in the OpenDOAR registry. The survey was distributed electronically. Recipients were asked to forward the email if they felt someone else was better suited to respond. There were questions about the demographics of the repository, the metadata creation environment, metadata quality, standards and practices, and obstacles to quality. Results were analyzed in Excel, and qualitative responses were coded by two researchers together. Main results – There was a 42% (n=21) response rate to the section on metadata quality, a 40% (n=20) response rate to the metadata creation section, and 40% (n=20) to the section on obstacles to quality. The majority of respondents rated their metadata quality as average (65%, n=13) or above average (30%, n=5). No one rated the quality as high or poor, while 10% (n=2) rated the quality as below average. The survey found that the majority of descriptive metadata was created by professional (84%, n=16) or paraprofessional (53%, n=10) library staff. Professional staff were commonly involved in creating administrative metadata, reviewing the metadata, and selecting standards and documentation. Department heads and advisory committees were also involved in standards and documentation selection. The majority of repositories used locally established standards (61%, n=11). When asked about obstacles to metadata quality, the majority identified time and staff hours (85%, n=17) as a barrier, as well as repository software (60%, n=12). When the responses to questions about obstacles to quality were tabulated with the responses to quality rating, time limitations and staff hours came out as the top or joint-top answer, regardless of the quality rating. Finally, the authors present a sample of responses to the question on how metadata could be improved and these offer some solutions to staffing issues, the application of standards, and the repository system in use. Conclusion – The authors conclude that staffing, standards, and systems are all concerns in providing quality metadata. However, they suggest that standards and software issues could be overcome if adequate numbers of qualified staff are in place.
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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 | 0,101 | 0,253 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,011 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,023 | 0,027 |
| Science ouverte | 0,005 | 0,014 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».