Measuring Usage: A Comprehensive Analysis of a Social Work Journal Collection
Bibliographic record
Abstract
This study examines what can be learned about a library’s electronic social work journal collection from usage statistics, survey data, faculty publications, and an examination of open access (OA) availability. A collections analysis was completed using data from two sources: a custom report by 1Science and results of a faculty survey on top journals for teaching. After creating a list of journals important to social work, top journals were identified by article downloads, faculty-authored publications, and references to faculty-authored papers. A publications analysis using faculty websites and author searches in Web of Science was also completed, to provide local, contextual data. SHERPA/RoMEO was used to determine the journals’ OA level and archiving policy. Library coverage for the journals was also included in the analysis. Results show that the McGill University Library has access to almost all of the journals identified as important to social work. Nearly one-third of publications authored by the McGill University School of Social Work since 2006 are OA, and more than half of the faculty in the school have at least one article published in an OA journal. While this is a good start for librarians who want to help faculty and students understand OA publishing and access, there is room for outreach in this area. While these results will aid librarians supporting faculty, students, and practitioners in the field of social work, a secondary aim of the study is to demonstrate a method that can be used by librarians undertaking similar analyses in other fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.042 | 0.050 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".