Navigating Domestic Violence Service Provision for Diasporic South Asian Communities in Canada: From the Perspectives of South Asian Service Providers Using a Multi-level Systems Approach and Culture Circles
Bibliographic record
Abstract
In Canada, domestic violence (DV) is a pervasive problem that impacts people regardless of their background. However, racialized communities can face additional challenges and discrimination when seeking DV resources and support, while service providers simultaneously struggle to accommodate and provide adequate and appropriate support. South Asian (SA) community members and scholars have highlighted the ongoing problems with DV conversations, policies, supports, and resources for the SA community. This study explores how SA service providers in Canada understand, interact with, and wish to improve DV supports for SA community members. This research is set within a critical-transformative paradigm and uses a qualitative research approach. Several theoretical frameworks guide this research, including a socio-ecological model, critical race theory, intersectionality, and radical imagination. Rooted in a Freirean approach to collective knowledge creation, I facilitated four culture circles with seven participants working in and from the SA community on issues of DV. These culture circles were followed by a final member check-in session. Additionally, I kept a reflective journal to document the research process. I then analyzed both the culture circles and my reflective journal as basis for this dissertation.\nThe overarching goal of this research was to shift harmful and individualized narratives of DV currently associated with the SA diaspora in Canada to one that focuses on structural and institutional systems, and how we, as a wider community, can address this multi-level and multi-faceted issue. Accordingly, and equally, my intention with this research is to provide a critical space in which we, as SA advocates, activists, and service providers can find space to debrief our experiences and learnings and begin to practically move towards challenging and recreating structures that no longer uphold systems that allow for 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.019 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".