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Record W2013520707 · doi:10.1177/0961000603353002

Canadian Federal Prison Libraries

2003· article· en· W2013520707 on OpenAlexaboutno aff
Ann Curry, Kris Glodoski Wolf, Sandra Boutilier, Helen Chan

Bibliographic record

VenueJournal of Librarianship and Information Science · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingPrisonAdministration (probate law)Context (archaeology)RecreationMaximum securityLibrary sciencePolitical scienceBusinessLawGeography

Abstract

fetched live from OpenAlex

A nationwide survey of the libraries in Canada’s 51 minimum, medium and maximum security federal correctional institutions was conducted in 2001 through a mailed questionnaire that received a 73 per cent response rate. The questionnaire was directed to the person in charge of the library. The survey gathered information about library staffing, library users, the size and composition of the collection, library funding and limitations on acquisitions and access imposed by the prison administration or the library staff because of subject matter, e.g. violent or sexual material. The results of the survey are set in the context of international prison library history and policy. Overall, the respondents said that their prison libraries were meeting offenders’ needs for recreational, cultural, educational and informative material, but that there was much room for improvement in funding for staff and collections. They also felt that their libraries were undervalued within the prison administration.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.481

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0150.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.003

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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 designQualitative
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".

Quick stats

Citations17
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Librarianship and Information ScienceSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207