Level 1 COUNTER Compliant Vendor Statistics are a Reliable Measure of Journal Usage A review of: Duy, Joanna and Liwen Vaughan. “Can Electronic Journal Usage Data Replace Citation Data as a Measure of Journal Use? An Empirical Examination.” The Journal of Academic Librarianship 32.5 (Sept. 2006): 512‐17.
Notice bibliographique
Résumé
Objective – To identify valid measures of journal usage by comparing citation data with print and electronic journal use data. Design – Bibliometric study. Setting – Large academic library in Canada. Subjects – Instances of use were collected from 11 print journals of the American Chemical Society (ACS), 9 print journals ofthe Royal Society of Chemistry (RSC), and electronic journals in chemistry and biochemistry from four publishers – ACS, RSC, Elsevier, and Wiley. ACS, Elsevier, and Wiley journals in chemistry‐related subject areas were sampled for Journal Impact Factors and citations data from the Institute for Scientific Information (ISI). Methods – Journal usage data were collected to determine if an association existed between: (1) print and electronic journal use; (2) electronic journal use and citations to journals by authors from the university; and (3) electronic journal use and Journal Impact Factors. Between June 2000 and September 2003, library staff recorded the re‐shelving of bound volumes and loose issues of 20 journal titles published by the ACS and the RSC.Electronic journal usage data were collected for journals published by ACS, RSC, Elsevier, and Wiley within the ISI‐defined chemistry and biochemistry subject area. Data were drawn from the publishers’ Level 1 COUNTER compliant usage statistics. These data equate 1 instance of use with a user viewing an HTML or PDF full text article. The period of data collection varied, but at least 2.5 years of data were collected for each publisher. Journal Impact Factors were collected for all ISI chemistry‐related journals published by ACS, Elsevier, and Wiley for the year 2001. Library Journal Utilization Reports (purchased from ISI) were used to determine the number of times researchers at the university cited journals in the same set of chemistry‐related journals over the period 1998 to 2002. The authors call this “local citation data” (512). The results from electronic journal use were also analysed for correlation with the total number of citations, as reported in the Journal Citation Reports, for each journal in the sample. Main results – The study found a significant correlation (p<0.01) between the results for print journal and electronic journal usage. A similar finding was reported for correlation between electronic journal usage data and local citation data (p<0.01). No significant association was found between Journal Impact Factors and electronic journal usage data. However, when an analysis was conducted for the total number of citations to the journals (drawn from the Journal Impact Factor calculations in Journal Citation Reports) and electronic journal use, significant correlations were found for all publishers’ journals. Conclusion – Within the fields of chemistry and biochemistry, electronic journal usage data provided by publishers are an equally valid method of determining journal usage as print journal re‐shelving data. The results of the study indicate this association is valid even when print journal subscriptions have ceased. Local citation data (the citations made by researchers at the institution being studied) also provide a valid measure of journal use when compared with electronic journal usage results. Journal Impact Factors should be used with caution when libraries are making journal collection decisions.
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 | 0,038 | 0,307 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,006 |
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 ».