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Record W6986284344

Opportunities for Solidarity with the Criminal Justice System Against Intimate Partner Violence; Why we Need to and How We Can

2016· dissertation· en· W6986284344 on OpenAlexaboutno aff

Bibliographic record

VenueNational University System Repository (National University System) · 2016
Typedissertation
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityCriminal justiceDomestic violenceEconomic JusticeTheory of criminal justiceValue (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

This thesis attempts to understand where the opportunities are for solidarity with the criminal justice system in an attempt to uphold the resistance of women experiencing intimate partner violence in Canada. As a country Canada spent more than $7.4 billion dollars to deal with the aftermath of intimate partner violence (Canadian Women's Foundation, 2014, p. 10). The Canadian Women’s Foundation (2014) believes that violence prevention programs and a strong criminal justice response can bring about an end to violence (p. 17). The criminal justice system is meant to be reflective of our values as a society. As a society we claim to value and support a culture of non-violence and anti-oppression for all members. We assume that the people working in the criminal justice system, including police, judges and court workers and advocates have an understanding of all individuals who come before them. This is clearly not the case as victims are less now likely to report abuse than they have been in the past (Canadian Women's Foundation, 2014, p. 11). If the criminal justice system is to be the macro level response to impact the lives of those impacted on micro levels by intimate partner violence, then we must provide the education, support and solidarity to those who are doing the work of the criminal justice system. Give them the tools and knowledge to provide the best possible response to a problem deeply rooted in our social values and norms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
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 head0.033
GPT teacher head0.229
Teacher spread0.196 · 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.

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
Published2016
Admission routes1
Has abstractyes

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