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At Your Leisure Pilot Project: Providing Leisure Reading Materials to a University Community through an Academic and Public Library Initiative

2017· article· en· W2746427567 on OpenAlexafffundvenue
Suzanne van den Hoogen, Kristel Fleuren-Hunter

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2017
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsSt Martha's Regional HospitalSt. Francis Xavier University
FundersUniversidad del AtlánticoSt. Francis Xavier University
KeywordsHumanitiesLibrary sciencePolitical scienceSociologyArtComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.255
GPT teacher head0.365
Teacher spread0.110 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations2
Published2017
Admission routes3
Has abstractyes

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