Starting With Life: Murder Sentencing and Feminist Prison Abolitionist Praxis
Bibliographic record
Abstract
Advocates of decarcation often focus their critiques on imprisonment for non-violent offences. In this vein, current advocacy efforts to end mandatory sentences in Canada tend to carve out “serious violent offences” as not part of a reform agenda. In this chapter, Debra Parkes sketches out the contours of an argument for why feminists might not want to cede that ground, why anti-carceral feminism might involve centering our analysis on the most, rather than the least, serious crimes – starting with those who are serving life sentences for murder. Parkes identifies four non-exhaustive reasons for that focus. The first reason relates to the problem of using state violence through incarceration to address interpersonal violence. The second is about who bears the brunt of these sentences: in Canada, Indigenous women make up nearly half of all women sentenced to life in recent years. The third points to what we learn, and what informs anti-carceral feminist praxis, when we center the people who are living these sentences. A final reason relates to what we might be able to achieve, in concrete terms, by seeking to abolish these sentences.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".