Extreme heat adaptation planning: a review of evaluation, monitoring, and reporting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extreme heat events are increasing in intensity and duration. Although heat adaptation planning is increasing across the US, the effectiveness of adaptation strategies across contexts remains unknown. Evaluation helps heat adaptation planners understand the impact of investments and increase accountability. To understand how evaluation is or is not happening in extreme heat planning, we purposively sampled and analyzed 65 plans that would likely include extreme heat adaptation strategies. We found that although 55% (n = 36) of plans included heat evaluation or monitoring plans in some form, fewer than 30% (n = 19) were associated with subsequent reports. Of these, only 6 were implemented as planned, and none were implemented at the regional or neighborhood level. We also found that monitoring indicators did not match the heat impacts, vulnerabilities, and needs identified in the plan. We provide evaluation recommendations to guide and support evaluation and monitoring efforts in the heat planning process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it