Canadian correctional program officers facilitating programming for sex offenders: The stickiness of stigma
Bibliographic record
Abstract
The stigma of being convicted (or suspected) of sex-related offenses is long recognized, resulting in people being viewed as being the most abhorrent of offenders. Sex offenders (SOs) have been studied, as well as the parole and correctional officers who work with them. Yet, despite their centralized role in prisoner rehabilitation, correctional program officers (CPOs) facilitating programs to SOs have yet to be examined in relation to their interpretations of SOs and of prison culture around SOs. Drawing on interviews with 12 CPOs who have delivered SO-focused programming to groups of SOs exclusively, we unpack the stigma and label of SOs in institutions and the community, the perceived implications of stigmatization on SO and CPOs, and on their ability to complete programs safely and successfully. Our findings reveal i) SOs remain stigmatized with repercussion for reintegration opportunities; ii) SOs remain at the bottom of the prisoner hierarchy; iii) SOs require support to physically attend programs; and iv) the SO stigma sticks to people facilitating sex offender programs. Findings are discussed in relation to stigma theories but also considerations for program delivery and CPO education and wellness are put forth.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".