Consulting the Community on Advancing an LGBTQ Alberta Framework on the Prevention of Domestic Violence
Bibliographic record
Abstract
This report contains the findings from a series of six consultations that Shift conducted throughout Alberta to better understand risk factors related to domestic violence victimization and perpetration within lesbian, gay, bisexual, transgender and queer (LGBTQ) communities, as well as barriers to help-seeking. A total of 81 individuals from across Alberta were involved in the consultations, including representatives from the LGBTQ communities, the domestic violence sector, health services, school systems and law enforcement. Participants agreed that there is a need for improved capacity among government and community-based organizations to provide better services to LGBTQ victims and perpetrators of domestic violence. In particular, many participants noted that a lack of appropriate and informed services presents a significant barrier to LGBTQ individuals who are trying to exit unhealthy relationships and/or violent circumstances. Domestic violence service providers themselves acknowledged the limitations of their knowledge about the unique experiences of LGBTQ individuals; however, these providers also demonstrated a genuine desire to learn about, and improve, the provision of care to prevent domestic violence within the LGBTQ community. Specific recommendations directed at the Government of Alberta and community-based agencies are included.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".