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Record W2756342087 · doi:10.1017/cls.2017.15

Appreciating Ashley: Learning About and From the Life and Death of Ashley Smith through Feminist Pedagogy

2017· article· en· W2756342087 on OpenAlexaff
Joanne Minaker

Bibliographic record

VenueCanadian Journal of Law and Society / Revue Canadienne Droit et Société · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCriminalizationInjusticeGender studiesSociologyPoliticsFeminismContext (archaeology)CriminologyEconomic JusticePolitical scienceLawHistory

Abstract

fetched live from OpenAlex

Abstract Feminist scholars and advocates struggle with how to confront the over-criminalization of the most marginalized girls and women. One of the most troubling illustrations of gross injustice is what happened to Ashley Smith. The anniversary of Ashley Smith’s death is a catalyst for amplifying feminist voices. In this paper, I use the Ashley Smith case as a way to frame how I teach critical social justice issues concerning the criminalization of girls and women. My aim is to encourage critical conversations about pedagogy in feminist criminology and socio-legal studies aimed at ameliorative change. With the discipline of Criminology’s systematic failure to understand the unique problems and shared circumstances of girls’ and women’s lives, feminist professors’ teaching, which offers a lens for our students that underscores young women’s constrained choices and the socio/political/cultural context in which their lives and behaviours are embedded, opens up possibilities for transformation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0150.024
Scholarly communication0.0070.007
Open science0.0010.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.032
GPT teacher head0.318
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Law and Society / Revue Canadienne Droit et SociétéSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207