Understanding the violence and help-seeking experiences of Mi’gmaq women: Qualitative and applied results from a partnered study in the community of Listuguj
Bibliographic record
Abstract
Indigenous women and girls in Canada are at increased risk of experiencing any type of violence throughout their lives. This pressing public health issue is receiving growing attention as Indigenous communities and others across the country and internationally continue to demand respect for Indigenous women’s human rights to health, safety, and justice. To support their claims, we conducted a five-year partnered study with Indigenous and non-Indigenous non-governmental organizations in the Mi’gmaq community of Listuguj, in the province of Québec. Our objective was to better understand the violence and help-seeking experiences of women from Listuguj, and to initiate actions to improve services offered to Mi’gmaq violence survivors. Based on interviews with violence survivors and workshops with service providers, we created an intervention tool to increase the collaboration among service providers in Listuguj in their actions against violence. This study, the first to focus on the services sought by Mi’gmaq women in Québec, presents another account of the interrelations between interpersonal and structural violence in the lives of Indigenous women. Its partnered approach led to applied results that can be used by Indigenous partners to better meet the needs of violence survivors and their families in their community.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".