Race in nature stewardship: an autoethnography of two racialised volunteers in urban ecology
Bibliographic record
Abstract
Abstract Urban nature stewardships can connect people to nature in their neighbourhood, foster a sense of belonging and citizenship, and increase well-being and place-making. This article examines how race intersects with urban nature stewardship, via a critical autoethnography by two co-authors who are racialised volunteers, Black and South Asian, in stewardship projects. Race is centered as a unit of analysis. In Toronto, Canada, racialised people are the majority of the population but are noticeable by their absence in nature stewardships and the broader environmentalism. Most urban nature stewardships operate on a colour-blind approach which masks how systemic racial inequities shape stewardship projects at the personal, place-making, and ecological levels. The article is illustrated by stewardship in tree planting and community gardens as urban ecology restoration projects. It concludes with some recommendations on how to engage racialised volunteers in nature stewardship.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".