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Record W4379977887 · doi:10.1093/bjsw/bcad140

The Contributions of First Nations Voices to the Australian Public Debate over the Criminalisation of Coercive Control

2023· article· en· W4379977887 on OpenAlexaboutno aff
Courtney Hobson, Michael Salter, Jennifer Stephenson

Bibliographic record

VenueThe British Journal of Social Work · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsPunitive damagesCrime controlGovernment (linguistics)Political scienceCriminologyIndigenousCriminal justiceRedressControl (management)LawSociology

Abstract

fetched live from OpenAlex

Abstract In Australia, there has been significant public debate over the criminalisation of coercive control and the impact on First Nations women and communities. This debate has included allegations of ‘carceral feminists’ using coercive control to advance a punitive agenda that harms First Nations communities. This article drew on the contributions to the public debate on coercive control by First Nations voices to amplify the views of First Nations women and organisations. The findings of the article identified limited and qualified support for criminalisation by First Nations contributors, with the majority of voices opposed to criminalisation, although both First Nations advocates and critics of criminalisation endorsed the need for alternatives to criminalisation. The article concludes that there is significant work to be done by government and non-indigenous organisations to establish criminal justice and child protection systems trusted by First Nations women and communities.

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)0.038
metaresearch head score (Gemma)0.047
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 score0.053
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.047
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0210.043
Scholarly communication0.0170.008
Open science0.0020.012
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
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

Citations4
Published2023
Admission routes1
Has abstractyes

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