Strong women’s circle: Supporting Alberta’s primary prevention framework by mapping root causes of violence and identifying policy recommendations to stop violence against Indigenous peoples before it starts
Bibliographic record
Abstract
This report informs the Alberta Primary Prevention Framework Collaborative project, a partnership between Shift, the Government of Alberta, and the IMPACT collective, focused on advancing upstream primary prevention efforts to stop violence before it starts. The research report centers Indigenous Peoples experiences and outlines primary prevention solutions that address the root causes of violence and promote systemic change. It builds on the rich work that has been accomplished by Indigenous researchers and activists from across Canada and reflects the expertise and lived experiences of three Indigenous researchers and eight Elders living in Alberta, who guided the research process. Created in ethical space, where Indigenous and Western worldviews came together to co-create sustainable anti-violence solutions, the report proposes five transformative changes to achieve Indigenous sovereignty along with a list of reforms for six systems that increase Indigenous Peoples’ vulnerability to violence: justice and policing, child welfare, health, education, housing, and transportation.
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 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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".