Domestic Violence in Immigrant Communities: Breaking the Cycle
Bibliographic record
Abstract
Dr. Ferzana Chaze and Dr. Bethany Osborne, and their fellow authors family lawyer Archana Medhekar, and Dr. Purnima George, Ryerson University discuss their recently published book Domestic Violence in Immigrant Communities: Case Studies. This book opens up an important conversation about the impact of domestic violence within immigrant communities and seeks solutions for how the social service and justice sectors can work more effectively to support vulnerable immigrant women and their families.\nIn this webinar they are joined by an exciting panel of experts, to discuss the importance and relevance of the topic, and possible next steps in breaking the cycle of violence. We will be welcoming Justice Gerri Wong from the Family Court; Lianne Kendall, Sheridan’s Sexual Violence Response Specialist; Antionette Clarke from Peel Family Mediation; and Professor Nick Bala from Queen’s University.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| 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".