Application of a Global Environmental Equity Index in Montreal: Diagnostic and Further Implications
Bibliographic record
Abstract
Urban living environments are known to influence human well-being and health. The literature on environmental equity focuses especially on the distribution of nuisances and resources, which, because of the unequal spatial distribution of different social groups, leads to an increased exposure to risks or to less access to beneficial elements for certain populations. Little work has been done on the multidimensionality of different environmental burdens and the lack of resources in some urban environments. This article has two main objectives. The first objective is to construct an environmental equity index that takes into consideration seven components of the urban environment (traffic-related pollutants, proximity to major roads and highways, vegetation, access to parks, access to supermarkets, and the urban heat island effect). The second objective is to determine whether groups vulnerable to different nuisances—namely, individuals under fifteen years old and the elderly—and those who tend to be located in the most problematic areas according to the environmental justice literature (i.e., visible minorities and low-income populations) are affected by environmental inequities associated with the application of the composite index at the city block level. The results obtained by using four statistical techniques show that, on the Island of Montreal, low-income persons and, to a lesser extent, visible minorities are more frequently located in city blocks close to major roads and with higher concentrations of NO2 and less vegetation. Finally, the environmental equity index is significantly lower in areas with high concentrations of low-income populations in comparison with the wealthiest areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".