At Your Leisure Pilot Project: Providing Leisure Reading Materials to a University Community through an Academic and Public Library Initiative
Bibliographic record
Abstract
Leisure reading collections were once as integral to academic libraries as they currently are to public libraries. This article examines the results of a partnership between an academic and a public library to provide access to leisure reading materials to a university community through a one-year pilot project. Data were collected using circulation statistics, gate counts, and comment cards in the form of book inserts. During the pilot, gate counts increased by 6%, and 91% of participants indicated that they would continue to use the collection often or sometimes. The pilot was officially adopted as a new service at the end of the one-year trial period. Auparavant, les collections de livres de détente faisaient partie intégrante des bibliothèques académiques tout comme elles font présentement partie des bibliothèques publiques. Cet article présente les résultats d’un partenariat entre une bibliothèque académique et une bibliothèque publique cherchant à donner accès à du matériel de lecture de détente à une communauté universitaire par le biais d’un projet pilote d’un an. Les données ont été recueillies en utilisant des statistiques de prêt, le nombre d’entrées et des commentaires reçus sur des formulaires insérés dans les livres. Durant le projet pilote, le nombre d’entrées a augmenté de 6% et 91% des participants ont indiqué qu’ils continueraient d’utiliser la collection souvent ou à l’occasion. Le projet a été adopté officiellement comme nouveau service suite au pilote d’un an.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".