Heatwave adaptation conditioned by everyday life: Analysing interacting changes to daily activities during Pacific Northwest summers
Bibliographic record
Abstract
As heatwaves increase in intensity, frequency, and duration, there is an urgent need for adaptation to limit their adverse effects on health, well-being, and livelihoods. Heat exposure and adaptive responses during heatwaves are tightly linked to mobility behaviours – the subject of a rapidly growing body of literature. However, knowledge of the processes which shape and constrain opportunities to seek cooling remains limited, as academic research has yet to examine how people alter the various activities of everyday life in response to heatwaves. Addressing this gap, the current paper models these interdependent activity changes simultaneously, shedding light on behavioural adaptations during heatwaves and the underlying structures which condition them. Combining Google Community Mobility Reports, ERA5 climate re-analysis, and socio-economic data across the Pacific Northwest region of North America, the analysis uses a multi-variate multi-level model to examine how anchor (home, work, transit), essential (grocery/pharmacy), and discretionary (retail/recreation, parks) activity change together during summer heatwaves. Focusing on a climatically diverse region and modelling heatwaves as distinct multi-day events, these interdependent responses are explored with the climatic, temporal, and contextual features of heatwaves. Four main conclusions about behavioural adaptation to heatwaves are drawn: (1) A region’s typical climate impacts workplace rigidity and adaptations to non-work activities during heatwaves; (2) Absolute and relative intensities have distinct yet comparably large impacts on behavioural responses; (3) Adaptation evolves over time, both between and within heatwaves; (4) Urban form and socio-economic disparities influence activity trade-offs during heatwaves. By contextualizing heatwaves within people’s everyday lives, this study highlights the diverse, dynamic, and yet constrained processes by which adaptation occurs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".