Ending the Silence: Responsive Community Support and Resources for Gender Based Violence
Bibliographic record
Abstract
On Friday, February 5, 2021 the Newcomer Student Association (NSA) in collaboration with the Ontario Council of Agencies Serving Immigrants (OCASI) under the 2020-2021 Immigrant and Refugee Communities Neighbours, Friends and Families (IRCNFF) Campaign hosted the second session of its 3-part webinar series, Ending the Silence. The focus of this session was on Responsive Community Support and Resources for Gender-Based Violence. The session was moderated by Dr. Alka Kumar—Manager of Research and Policy at NSA. The three panelists included two NSA team members—Dr. Rahbari-Jawoko (Ryerson University Professor and Manager, Strategic Initiatives at NSA) and Jaspreet Kaur—(NSA Manager, Programs and Events, Newcomer Resilience Award recipient and research contributor to Domestic Violence in Immigrant Communities: Case Studies project) as well as Sidrah Ahmed-Chan, a public educator, researcher and writer with expertise in survivors of Islamophobic violence. The panelists drew from their professional and practice expertise and respectively commenced their presentations with discussion of the various ways intimate partner violence (IPV) generally manifests in a relationship and called attention to what is needed to mitigate challenges posed by the COVID-19 pandemic and existing community capacity building tools and resources.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".