“I’m in a Federal Prison, and I’ve Never Felt More Free”
Bibliographic record
Abstract
Abstract Gresham Sykes famously identified five key deprivations of imprisonment, understood as characteristic of prison life. Recent work has extended this analysis to the distinctive pains endured by female prisoners, although little consideration has been given to the question of how incarcerated women relate to prison vis-à-vis their past traumatic experiences. This chapter foregrounds the experiences of Indigenous women serving sentences in a women’s federal prison in Alberta, Canada. We ask: How do our participants experience prison in the context of their life histories in a settler-colonial society, and how might Sykes’s five pains of imprisonment map onto their experiences? Our findings suggest that Sykes’s formulation is certainly part of the story relating to their distinctive pains, but often only a small part. The women we interviewed consistently offered countervailing narratives about their relationship to prison, and to pain more generally, that need to be understood in the context of settler colonialism. We offer cautions about the generalizability of Sykes’s “pains” framing while also contributing to our evolving understanding of the experiential dimension of settler colonialism in Canada.
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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 | 0.000 | 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 | 0.007 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".