Leisure reading collections in academic health sciences and science libraries: results of visits to seven libraries
Bibliographic record
Abstract
OBJECTIVE: To visit leisure reading collections in academic science and health sciences libraries to determine how they function and what role they play in their libraries. METHODS: The author visited seven libraries with leisure reading collections and carried out a semistructured interview with those responsible either for selection of materials or for the establishment of the collection. RESULTS: These collections contained a variety of materials, with some libraries focusing on health-science-related materials and others on providing recreational reading. The size of the collections also varied, from 186 to 9700 books, with corresponding differences in budget size. All collections were housed apart, with the same loan period as the regular collection. No collections contained electronic materials. Although there was little comparable statistical data on usage, at the six libraries at which active selection was occurring, librarians and library staff felt that the collection was well used and felt that it provided library users with benefits such as stress relief and relaxation and exposure to other perspectives. CONCLUSION: Librarians and library staff at the libraries that undertook active selection felt that their leisure reading collection was worthwhile. It would be interesting for future work to focus on the user experience of such collections.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".