Centering Community Perspectives to Advance Recognitional Justice for Sustainable Cities: Lessons from Urban Forest Practice
Bibliographic record
Abstract
Cities worldwide are grappling with complex urban environmental injustices. While environmental justice as a concept has gained prominence in both academia and policy, operationalizing and implementing environmental justice principles and norms remains underexplored. Notably, less attention has been given to centering the perspectives and experiences of community-based actors operating at the grassroots level, who can inform and strengthen urban environmental justice practice. Through ethnographic, participant-as-observer methods, interviews, and geovisualizations, this study explores the perspectives, experiences, knowledge, and practices of community-based urban forest stewards in Philadelphia, Pennsylvania (United States) who are invested in addressing environmental injustices through urban tree-planting and stewardship. Interviewees were asked how they were addressing issues of distribution, procedure, and recognition in urban forest planning and practice, as well as the socio-political and institutional factors that have influenced their perspectives and practices. Particular attention is given to how urban forest stewards implement recognitional justice principles. Findings from this study exposed several complex socio-political challenges affecting steward engagement in community-led tree initiatives and the broader pursuit of environmental justice, including discriminatory urban planning practices, gentrification concerns, underrepresentation of Black and Latinx voices in decision-making, volunteer-based tree-planting models, and tree life cycle costs. Nevertheless, urban forest stewards remain dedicated to collective community-building to address environmental injustices and stress the importance of recognizing, listening to, dialoguing with, and validating the perspectives and experiences of their neighbors as essential to their process.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| 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".