Incarcerated Women and Leisure: Making Good Girls Out of Bad?
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 it