Canadian Prison Environments: A Mixed Methods Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current study uses a mixed methods approach to assess quality of prison life in Canada's Atlantic provincial correctional institutions. Questions from the Measurement of the Quality of Prison Life were adapted to create scales to assess prisoner climate dimension perceptions, with open-ended questions providing qualitative data. Across eight prison sites, statistical analysis revealed between prison differences and confirmed that prison sentence location did matter. The qualitative data emergent themes also produced several consistent concerns that Likert responses could not capture, ranging from primary needs to service desires. Research affirms the importance of studying prison environments and supports the use of mixed methods, as qualitative data can provide greater insight into the lived experience of inmates and better chart change that is beneficial to them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it