Bibliographic record
Abstract
The current pandemic is a stark reminder that crises bring to light society's vulnerabilities. In the lead paper of this issue of Healthcare Papers, Miller and Xie (2020) argue that the same is - and will be - true for climate change. They make a compelling and urgent case for its importance to health and healthcare in Canada and around the world. Opportunities to advance the multiple interrelated dimensions of sustainability in the health sector include understanding and mitigating the health implications of climate change; preparing the health sector for climate change; and accelerating the health sector's contribution to society-wide net-zero targets. High-performing, resilient health systems with their capacity to deeply engage with communities, and to respond dynamically to changing circumstances, will be key to proactively addressing climate change, just as they are proving to be in pandemic preparedness and response.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.097 | 0.065 |
| Insufficient payload (model declined to judge) | 0.022 | 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".