Application of Intersectional Analysis to Data on Domestic Violence Against Aboriginal Women Living in Remote Communities in the Province of Quebec
Bibliographic record
Abstract
This article discusses the theoretical and analytical intersectionality approach, focusing on its application to an analysis of empirical data obtained from qualitative research into domestic violence against Aboriginal women living in four remote communities in Quebec. Nonprobability sampling was used to select and recruit 40 participants. Four focus groups took place, one in each of the participating communities. The qualitative data were subjected to a thematic content analysis emphasizing the feminist intersectionality perspective. The findings revealed the existence of different domination systems, as well as oppressive actions that interlock and interact at multiple and shifting levels, all of which shape and contribute to the reproduction of domestic violence among women living in remote Aboriginal communities. The intersectionality approach highlighted the important role played not only by race, gender, and social class, but also by the historical context and the degree of geographic isolation in the domestic violence experienced by Aboriginal women living in remote communities. All these social systems increase the vulnerability of Aboriginal women to domestic violence. This paper is one of the few scholarly attempts made so far to apply intersectional analysis to empirical data on the phenomenon of domestic violence as experienced by Aboriginal women.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| 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".