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Record W2911852182 · doi:10.5663/aps.v7i2.28897

How can community-university engagement address family violence prevention? One child at a time.

2019· article· en· W2911852182 on OpenAlexaffvenue
Linda DeRiviere

Bibliographic record

Venueaboriginal policy studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsDisadvantagedIndigenousPovertyUnemploymentDomestic violenceCommunity developmentCommunity engagementSociologyEconomic growthPolitical scienceCriminologyPedagogyMedicinePoison controlPublic relationsSuicide preventionEnvironmental health

Abstract

fetched live from OpenAlex

Family violence in Indigenous communities is one of the most pressing policy challenges of our times. This issue is highly related to the stressors caused by the disadvantaged socio-economic circumstances of Indigenous peoples, such as poverty and unemployment, and community trauma attributed to colonization and a loss of culture. This article is a case study based on the evaluations of four community-university engagement initiatives for Indigenous children, youth, and their families at a small inner-city university. It documents six principles for policy development used to engage students in their education and to begin to perceive themselves as high school and post-secondary graduates. These programs are just a few examples of how a small inner-city university took an imaginative community development approach to promoting social change, with each program tantamount to an anti-violence strategy.

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)0.007
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0070.005
Open science0.0020.015
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.056
GPT teacher head0.356
Teacher spread0.300 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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