Extreme heat events and health vulnerabilities among immigrant and newcomer populations
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
With higher temperatures linked to increased human morbidity and mortality, the projected increase in the number of extreme heat events (EHEs) due to climate change poses increased risks. Although the old, individuals with pre-existing illnesses, the socially isolated, and individuals with low income or low educational status are more vulnerable to the health effects of EHEs and are targeted in public health messaging, newcomers and immigrants may be less aware of the dangers of EHEs. The impacts of EHEs on the immigrant and newcomer population are not well documented in the Canadian context and the combination of a greater number of heat events and a growing and diverse immigrant population necessitates further exploration. Framed by intersectionality and using Hamilton, Ontario, as a case example, this work explores the barriers that may affect immigrant’s awareness of EHEs.
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 | 0.001 | 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 | 0.001 | 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) | 0.003 | 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