Transferring injury data to decision makers in British Columbia
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Acknowledgements This case study is part of an ongoing research programme entitled ‘Burden of Injury in BC and Its Local Communities: Information and Evidence for Community-based Prevention Strategy, Health Policy and Service Provision’. Ethics approval was granted by the University of British Columbia Behavioural Research Ethics Board. Craig Mitton receives funding from the Michael Smith Foundation for Health Research and the Canada Research Chairs program. Ying MacNab receives funding from the the British Columbia Child and Family Research Institute Investigator Award program, the Natural Sciences and Engineering Research Council of Canada, and the Canadian Institute for Health Research, and the Michael Smith Foundation for Health. Les Foster receives financial support from the British Columbia Ministry of Health. The authors also thank the British Columbia Ministry of Health, the British Columbia Vital Statistics Agency and the University of British Columbia's Centre for Health Services and Policy Research for provision of the injury data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".