MétaCan
Menu
Back to cohort
Record W6987318922

Starting With Life: Murder Sentencing and Feminist Prison Abolitionist Praxis

2021· article· en· W6987318922 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsImprisonmentPraxisArgument (complex analysis)FeminismPrisonState (computer science)Indigenous
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.245
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueeYLS (Yale Law School)Same topicCell Image Analysis TechniquesFrench-language works237,207