Pursuit of environmental justice in urban forest planning and practice
Bibliographic record
Abstract
Introduction There is a growing demand for urban forest management that prioritizes genuine community involvement, acknowledges power imbalances within society, and embraces the principles of environmental justice. To assess current initiatives and share better/best approaches, examining how environmental justice principles are applied in urban forest planning and practice is crucial. This study aims to understand the perspectives of urban foresters on the factors that either facilitate or impede the attainment of environmental justice goals. Methods Interviews were conducted with urban foresters from non-profit organizations and municipal government in San Francisco, California, and Seattle, Washington. The interviewees were asked to identify and discuss their tree planting and maintenance strategies, public engagement protocol, and inter-organizational collaboration processes. To provide a contextual understanding of environmental injustice in the study cities, the historical racist practice of neighborhood redlining was examined alongside current tree canopy cover, locations of environmental hazards, and the spatial distribution of persons of color and those living in poverty. Results The findings revealed that urban forestry professionals in each city approached environmental justice in distinct yet complementary ways: San Francisco prioritized distributional justice, while Seattle focused on elements of procedural and recognitional justice. The Race and Social Justice Initiative in Seattle and Proposition E in San Francisco have been instrumental in identifying and addressing inequities in urban forest planning and practice. Discussion/conclusion Creating fair and inclusive urban forestry practices that prioritize disadvantaged neighborhoods has been a difficult task for both cities. Acknowledging and addressing past policies and cultural perspectives that have led to marginalization is crucial for building trust with these communities. Moving forward, prioritizing recognitional justice in urban forest planning and management should be a top priority.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".