Towards a Systematic Review of Environmental Injustice in Canada: National Patterns of Environmental Risks and Benefits
Bibliographic record
Abstract
The environmental justice paradigm, now more than three decades old, is gaining new momentum by expanding to include a wider range of issues, broader geographic scope, and greater focus on potential solutions. However, to date, most research on environmental injustice in Canada has focused on specific case studies (e.g., large urban centres, Indigenous communities), with only a small number of studies focusing on national or regional-scale patterns. As part of a larger interdisciplinary effort, combining quantitative, qualitative, and legal analysis, this research will provide a deeper understanding of uniquely Canadian environmental injustice patterns and identify potential case studies for future work on the processes that create and perpetuate these injustices.In this preliminary analysis, we describe how exposure to selected environmental hazards (e.g., ground-level ozone, water advisories) and access to environmental benefits (e.g., green space) varies across demographic and socioeconomic characteristics in Canada, at a national-scale. We apply methodologies (described below) from recent national-scale analyses of NO2 and fine particulate matter in the US and Canada to data from the 2016 census and existing environmental data sets from the Canadian Urban Environmental Health (CANUE) Research Consortium, and from federal and provincial governments. Population variables considered include Aboriginal and racialization status, immigrant status, socio-economic status, age, stratified by urban and rural residence. We compute descriptive statistics (Student’s t-test and Cohen’s d) to evaluate the significance and magnitude of differences between selected groupings. Finally, we situate these results within our larger framework for a systematic analysis of environmental injustice in Canada by discussing linkages to law, policy, and social movements that may contribute to, or remedy, these observed patterns.
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 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) | 0.002 | 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) | 0.001 | 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".