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Record W2802770037 · doi:10.11575/prism/31393

Domestic Violence in Alberta’s Gender and Sexually Diverse Communities: Towards a Framework for Prevention

2015· article· en· W2802770037 on OpenAlexaboutno aff
Liza Lorenzetti, Lana Wells, Tonya D. Callaghan, Carmen H. Logie

Bibliographic record

VenueOpen MIND · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsExecutive directorGovernment (linguistics)Domestic violenceLibrary sciencePolitical scienceSociologyManagementGender studiesSuicide preventionPoison controlMedicine

Abstract

fetched live from OpenAlex

This report provides an overview of domestic violence within gender and sexually diverse communities, with a focus on Alberta and Canada. Included are specific risk factors for gender and sexually diverse communities, as well as information about barriers to accessing safe and appropriate services. The report highlights areas for prevention, including promising practices aimed at decreasing rates of violence, promoting attitudinal and norms change, and providing safe, welcoming and appropriate domestic violence services. The findings from this report are currently being shared across Alberta with the objective of catalyzing a much-needed discussion about how discrimination, stigma and systemic barriers negatively impact the lives of gender and sexually diverse communities. Pam Krause, President and CEO of the Calgary Sexual Health Centre and Brian Hansen, Shift Research Associate have been leading a series of consultations across Alberta, sharing the research findings and trying to identify solutions at the local and policy levels. If you would like more information, please contact bhansen@ucalgary.ca

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.179
GPT teacher head0.440
Teacher spread0.261 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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