Where Heat Doesn't Come in Waves: A Framework for Understanding and Managing Chronic Heat
Bibliographic record
Abstract
Research on heat and its risks has focused on heat waves as an increasing emergency under climate change, but this emphasis has obscured the chronic-not just acute and episodicexposure of millions of people globally to increasingly dangerous levels of heat. In many regions, predominantly in the global tropics, heat index exceeds a level of extreme caution according to the U.S. National Weather Service (90°F, 32.2°C) for more than an entire season and in some cases for much of the year. We propose chronic heat as an alternative framing for heat-related hazards in these regions and demonstrate how its risks differ and are incompletely captured by current heat-health research practices. Chronic heat poses unique risks compared to acute heat because the intersection of enduring societal-and individual-level factors leads to substantially divergent cumulative exposures over seasonal timeframes and associated health outcomes, quality-of-life impacts, and tradeoffs. These multiple interacting factors are difficult to tease out and attribute with traditional heat-health research practices, and therefore understanding of the impacts of chronic heat has remained poor. Further, managing chronic heat requires use of social services, programs, and partners not previously engaged in the context of heat, going beyond heat response as emergency management. Our chronic heat framework identifies a shift needed in heat research and practice to understand and address chronic and cumulative heat exposures increasingly experienced worldwide under intensifying climate change.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".