Incarcerated Women and Leisure: Making Good Girls Out of Bad?
Bibliographic record
Abstract
Women in prison are among the most marginalized of populations, and the general perception of women who have come into contact with the criminal justice system as either mad or bad is fairly persistent. Therapeutic interventions that are in place for this population are designed with rehabilitation and re-integration in mind. In large part, the rationale for this is that these women will one day return to the community, and the goal is to ensure that their behaviour is ‘normalized’ so they can return as law-abiding citizens. This exploration critically examines a leisure intervention known as Stride that is brought into a federal prison for women in Canada. Using data from qualitative interviews, the paper employs the women’s voices to consider whether the leisure and recreation they experience through the Stride intervention is functioning to normalize behaviour and ultimately make good girls out of women who are deemed bad by society. The authors, employing critical criminology and creative analytical practice, conclude that leisure and recreation opportunities do not seek to change or normalize behaviour of the women. Instead, the activities provide a setting for recreation participation that fosters friendships among incarcerated women and women in the community. Implications for practice point to the relevance of informal opportunities for recreation participation and friendship development, which can provide critical support to women in their reintegration efforts once released from prison.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".