Exploring attention to justice, equity, diversity and inclusion in Canadian environmental non-profits: implications for racialised migrants
Bibliographic record
Abstract
In recent years, increasing calls have been made for environmental justice in Canada and around the world amidst rapidly diversifying populations, growing environmental inequalities, and expansion of the environmental sustainability sector in response to the climate crisis. Racialised migrant communities face disproportionate exposure to environmental risks while also facing exclusion from well-paid, meaningful opportunities in the environmental sector. The Canadian labour market relies heavily upon highly skilled racialised migrant workers for economic growth and has recently increased investments in expanding the green economy. Yet, there remains limited understanding of the extent to which environmental organisations in Canada are making progress on justice, equity, diversity and inclusion (JEDI) goals and how. We draw upon interviews with environmental professionals in leadership roles (n = 14) and focus groups with racialised migrants (n = 15) to explore challenges and best practices for integrating JEDI policies in environmental organisations with emphasis on the non-profit sector. Environmental justice literature considers the experiences of racialised communities, yet with limited focus on the unique challenges related to immigration, settlement and integration. On the other hand, immigration researchers that document the challenges of economic integration pay little attention to the environmental sector specifically, despite known, widespread environmental injustices facing migrant and racialised populations. We discuss how to strengthen the hiring of racialised migrants in the Canadian environmental non-profit sector, inclusive participation in environmental-related programming, and the role of Canadian environmental nonprofits in strengthening procedural, recognitional and distributional environmental justice.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.060 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".