Methodological Challenges in Collaborative Research with Immigrant Women Experiencing Intimate Partner Violence in Canada
Bibliographic record
Abstract
Abstract Purpose – The chapter explores the methodological challenges in doing community-based participatory research (CBPR) in social science investigations with immigrant women experiencing intimate partner violence (IPV) in Canada. Methodology/approach – The methodological comments, observations, and challenges discussed in this chapter result from research funded by the Social Science and Humanities Council, a branch of the Canadian Federal Tri-Council. The research that the authors conducted was both quantitative and qualitative in nature. The sample consisted of three groups of women: (1) immigrant women in Canada >10 years, (2) immigrant women in Canada <10 years, and (3) visible minority women born in Canada. Findings – The chapter highlights some of the lessons learned in conducting CBPR research in the context of immigrant survivors of IPV. This discussion can be relevant to both academics and non-profit/advocacy agencies interested in pursuing community partnership research on interpersonal violence. Originality/value – There is a paucity of writings on CBPR research in the social science and the challenges. This chapter reveals the methodological challenges that the researchers experienced in doing CBPR with racialized immigrant women who are survivors of IPV. This discussion can be relevant to both academics and non-profit/advocacy agencies interested in pursuing community partnership research on interpersonal violence.
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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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".