Experiences of Learners Who Are Incarcerated With Accessing Educational Opportunities in Ontario, Canada
Bibliographic record
Abstract
Access to education is a human right that should be upheld for everyone including individuals who are incarcerated as outlined in Article 26 of the United Nations Universal Declaration of Human Rights. 25 interviews were conducted between April to June 2021 with various key stakeholders: 5 staff involved with the delivery of educational programs in jails, 10 learners who are or were formerly incarcerated, and 10 representatives from post-secondary institutions or jails. The objective was to identify barriers limiting access to education, while incarcerated and post-release, and how such barriers can be mitigated. Responses were examined using Critical Race Theory as a paradigm and thematic analysis as a methodology. Findings indicate that access to education for individuals who are incarcerated remains limited, not prioritized, and overall an underdeveloped sector in Canada. More funding and resources need to be allocated to prioritize education and expand the capacity of incarceration facilities to offer more programming in ways that are accessible and socio-culturally relevant.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".