“Participation”, White Privilege and Environmental Justice: Understanding Environmentalism Among Hispanics in Toronto
Bibliographic record
Abstract
Abstract: The environmental justice movement has highlighted not only the unequal distribution of environmental hazards across lines of race and class, but also the white, middle‐class nature of some environmentalisms, and broader patterns of marginalization underlying people's opportunities to participate or not. There is a significant body of work discussing Hispanic environmental justice activism in the US, but not in Canada. This paper draws on interviews with representatives of organizations working on environmental initiatives within the Hispanic population of Toronto, Canada to explore definitions of and approaches to environmentalism(s) and community engagement. Four interrelated “mechanisms of exclusion” are identified in this case study—economic marginalization; (in)accessibility of typical avenues of participation; narrow definitions of “environmentalism” among environmental organizations; and the perceived whiteness of the environmental movement. Taken together, these mechanisms were perceived as limiting factors to environmental activism in Toronto's Hispanic population. We conclude that the unique context of Toronto's Hispanic community, including contested definitions of “community” itself, presents both challenges and opportunities for a more inclusive environmentalism, and argue for the value of “recognition” and “environmental racialization” frameworks in understanding environmental injustice in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".