Bibliographic record
Abstract
As a society, we sometimes find it difficult to grapple with the realities of our correc-tional system. Too often our perspective is informed (or in many cases mis-informed) by depictions of prisons and prison life that we encounter through films, television programming and in the print media. These accounts (actual and fictional) have a shared tendency to sensationalize and at the same time simplify the issues faced by incar-cerated individuals and those who work with them. The lens of our collective values and beliefs about the nature of and reasons for incarceration may blind us to the realities of the activities that occur in prisons, the impacts these activities have on inmates, the prison ‘community’, and the broader community outside the walls. A tangible example of this is the issue of illicit drug use in prisons. One could not be faulted for assuming that in a highly restricted and controlled environment such as a prison, illicit drugs would not be an issue – they are secured environments. Yet, correction-al facilities, in Canada and elsewhere, are clearly places where substance use and the use of illicit injection drugs occurs on a regular basis, despite best efforts to curtail it. Studies sug-gest that 50-70 % of inmates in federal prisons in Canada are substance dependent and/or require treatment for an alcohol and/or substance use disorder.1,2 Recent studies on drug use in both federal and provincial prisons show that between 19 and 32 % of drug-using
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.324 | 0.150 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".