Race in nature stewardship: an autoethnography of two racialised volunteers in urban ecology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it