ENVIRONMENTAL JUSTICE IN CANADA: THE STATISTICAL CORRELATION BETWEEN SOCIOECONOMIC STATUS AND POLLUTION IN FIVE MAJOR CANADIAN CITIES
Bibliographic record
Abstract
This thesis explores the existence of environmental injustice in five Canadian census metropolitan areas (CMA): Edmonton, London, Montreal, Toronto and Victoria. Spatial/distributive environmental injustice refers to situations where specific, already disadvantaged, groups bear a disproportionate share of pollution exposure. Several studies in the United States have found that pollution is often more prevalent in areas that are predominantly low income backgrounds or are visible minorities (UCC, 1987; Mohai and Bryant, 1992; Szasz and Meuser, 1997; Ringquist, 2005). Though there are limitations to the ecological approach typically used in these cases, important lessons from the US research (e.g., Bowen, 2000; Maantay 2002; Mohai and Saha 2006) can be\napplied in the Canadian context. The present study builds on these lessons to contribute to a growing set of Canadian studies by ‘weighting’ pollution values according to toxic intensity. Data from 2001 Canadian Censuses are regressed against GIS-derived census tract exposures from the National Pollutant Release Inventory (NPRI) (point source industrial pollution) and from DMTI’s road network (traffic pollution). The independent variables are percentage of manufacturing employment, percentage of visible minority, percentage of aboriginal identity, percentage of recent immigrants, percentage of immigrants, percentage of lone-parent families, percentage of low-income families, median household income, average dwelling value and population density. The results indicate statistically significant correlations between median household income, percentage of immigrants and percentage of visible minorities, and industrial pollution exposure. However, in general, there is no consistent evidence of environmental injustice\niii\nwith respect to the other variables across the five cities as the result varies widely across the CMAs and buffer sizes. The control variable, population density is the strongest (negative) and most consistent predictor of pollution across the CMAs
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".