Correctional Service of Canada Prison Libraries from 1980 to 2010
Bibliographic record
Abstract
The last three decades have seen many developments in Canadian prison libraries. This article follows the history of the libraries in federal Correctional Service of Canada (CSC) from the 1980s to the present, concentrating on the libraries in the Pacific Region. A chronological overview of the major legislative changes, reports, and events of the last thirty years highlights the increased profile of prison libraries and their role in supporting Correctional Service of Canada's Mission and Goals. Some of these changes include the adoption in 1992 of the Corrections and Condition Release Act (CCRA) and Regulations, modifications to Commissioner's Directive 720 (2007a; under which libraries fall), and the adoption in the Pacific Region of Library Policy Guidelines. In addition to legislative and policy changes, Canadian society itself has also changed during this thirty-year period. As the face of Canada has become more diverse in age and ethnicity, as well as in social and technological expectations, so has the face of the prison population. These changes have, of course, also impacted on prison libraries. This article examines how prison libraries have met the challenges created by these societal and technological changes.
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 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".