Montreal's environmental justice problem with respect to the urban heat island phenomenon
Bibliographic record
Abstract
Due to climate change, heat events in Canada have become more extreme in intensity and frequency and will continue to do so according to the Intergovernmental Panel on Climate Change's global predictions. Environmental justice research has indicated that extreme heat exposure disproportionally affects socio‐economically disadvantaged populations in cities. The objective of this research was to determine whether such a phenomenon exists in Montreal, Canada. Temperature data were obtained through in‐situ sensors and governmental weather stations, while census data were retrieved from Statistics Canada through the Census mapper. Correlation tests were run between temperature and five demographic and socio‐economic variables measured inside a 500 m buffer around the temperature sensors. The variables included Indigenous Peoples (IND), people of 65 years old and over (Over 65), people between 25 and 64 years old without a high school degree (No HS), and low‐income (LI). A positive correlation was found for LI and No HS (p < 0.05). A regression test performed with interpolated temperature and the demographic and socio‐economic variables across the study area revealed no significant correlation due to spatial heterogeneity.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".