Reply to Commentary response by Wanigaratne, Mawani, O'Campo,<i>et al</i>
Bibliographic record
Abstract
We thank Wanigaratne and Mawani et al for taking the time to write this Commentary,1 which we have read with great interest. We agree that the framing and interpretation of findings about immigrant and refugee communities is of great importance and appreciate the opportunity to provide clarification. We would first like to acknowledge the valuable expertise of the authors as well as their strong relationships and vital advocacy work within communities. The primary aim of our study was to provide descriptive epidemiology of homicide in Ontario.2 Very few population-level descriptive studies have been published characterising homicides, particularly regarding trends in homicide victimisation between and across population subgroups. Our study team includes epidemiologists, professional and academics who work at the intersection of public health and violence, experience with implementing violence prevention programmes in marginalised populations around the world and expertise in working with large linked health administrative data. The linked health and administrative databases we used help fill the data gap with respect to understanding the victims of violence, including but not limited to refugee status.3 This aim is consistent with other descriptive database studies published about health and health system outcomes among immigrant and refugee populations in Ontario.4–11 The motivation for this study was to provide descriptive data that can be used by communities and researchers to better understand the distribution of health outcomes across populations. Our study found differences in risk of homicide across several social and economic indicators, including lower socioeconomic …
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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 | 0.011 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.038 | 0.053 |
| Insufficient payload (model declined to judge) | 0.013 | 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".