Vulnerable Census Tracts and Poor Air Quality: An Investigation in Montreal, Toronto, and Vancouver (2001, 2006, 2011, and 2016)
Bibliographic record
Abstract
Exposure to fine particulate matter (PM2.5) has deadly health outcomes. Canada’s disparities in air pollution exposure have been investigated along different vulnerability categories. Renter status, low-income status, and dwelling value, the vulnerability variables in this study, have been linked to other social and environmental disparities in Canada. This thesis explores PM2.5 exposure at the census tract level in the Montreal, Toronto, and Vancouver metropolitan areas for 2001, 2006, 2011, and 2016 using these vulnerability variables. I investigated these relationships between the vulnerability variables and PM2.5 using correlations and the bivariant local Moran’s I. I found consistent correlations over time between these vulnerability variables and PM2.5 exposure. The spatial clustering was linked more to city center location than vulnerability metrics. The results in Montreal were unique, so I studied Montreal further in a regression analysis. This longitudinal cross-city study bettered understanding of the relationships between social vulnerabilities and air pollution.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".