Commentary in response to ‘characterising the risk of homicide in a population-based cohort’ (O’Neill <i>et al</i>, 2019)
Bibliographic record
Abstract
We are social epidemiologists and community advocates focused on addressing social determinants of health inequities. While we appreciate O’Neill et al ’s effort to link multiple provincial-level administrative data sets to examine homicide victimisation by immigration status in Ontario, Canada, we have concerns about the framing and interpretation of findings and their potential impact on immigrants and refugees.1 While O’Neill et al ’s data and sample size are strengths, the attention to the context of being an immigrant to Canada, theoretical framework and motivation for examining immigrants in relation to homicide victimisation are not fully developed. O’Neill et al do not acknowledge having done any community engagement which is critical and ethical2 given the long history of exclusion, exploitation, racism and discrimination, and the current global climate of increasing criminalisation of migrants. Meaningful community engagement offers important context; helps shape the research purpose, questions, approach, interpretation and recommendations; and can reduce the potential for harm. Though criminalisation of migration under security pretexts is an infringement of international law,3 and contradicts evidence that immigration is related to a reduction in crime,4 many high-income countries, including Canada, …
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.049 | 0.048 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".