Climate, marginalization, and mental health in Canada
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
Climate change is regarded as the most significant threat to human health in this century. There is a growing consensus that high temperatures in particular have a significant, negative effect on mental health outcomes. Work in environmental justice has shown the unequal distribution of the impacts of climate change, people who are already experiencing social marginalization tend to experience the worst effects. In this thesis, I explore climate change and health in Canada by asking if air temperature affects mental health, and testing for effect variation across three primary dimensions of social marginalization—race, gender, and income. Using a custom dataset that brings together historical weather data and survey data from the Canadian Community Health Survey, I use multivariate models to estimate the effects of absolute and relative temperature on two measures of mental health. I fail to find robust evidence for a relationship between temperature and mental health, and find none for the differential impact of temperature on mental health by race, gender, or income. These results challenge a growing body of research which shows that temperature is significantly correlated with negative mental health outcomes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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