Bibliographic record
Abstract
This research provides a local case study of responses to ‘gender’ and ‘diversity’ within Canada’s federal parole system. I examine the following questions: How are certain ‘differences’ and categories of offenders constituted as targets for ‘accommodation’ or as having ‘special needs’? How do penal institutions frame ‘culturally relevant’ or ‘gender responsive’ policy and, in doing so, use normative ideals and selective knowledge of gender, race, culture, ethnicity, and other social relations to constitute the identities of particular groups of offenders? I explore these questions by tracing the history of policy discussions about gender and facets of diversity within legislation and penal and parole policies and practices, as well as the current approaches to managing difference used by the National Parole Board (NPB). Specific focus is given to the organizational responses and approaches developed for Aboriginal, female, and ‘ethnocultural’ offenders.\nIn this study, I show that the incorporation of diversity into the federal parole system works to address a variety of organizational objectives and interests, including fulfilling the legislative mandate to recognize and respond to diversity; appealing to human rights ideals and notions of fairness; managing reputational risk and conforming to managerial logics; instituting ‘effective’ correctional practice; and addressing issues of representation. At the same time, the recognition of gender and diversity produces new penal subjectivities, discourses, and sites upon which to govern. I argue that the accommodation of gender and diversity provides a narrative of conditional release and an institutional framework that positions the NPB as responsive to the diverse needs and/or experiences of non-white and non-male offenders. In the Canadian context, the penal system strives to deliver ‘fair’ punishment through the selective inclusion of difference, and without altering or reconsidering fundamental structures, practices, and power arrangements. Diversity and difference are instead added onto and/or incorporated into preexisting penal policy and logics, including risk management and managerialism.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.076 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".