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 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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".