Environmental Justice and Collaborative Governance: Building a Socio-Spatial Perspective for Facility Siting
Bibliographic record
Abstract
Environmental justice concerns, in part, the distribution of both environmental hazards and nuisances such that impoverished communities, particularly impoverished communities of color, contend more with the effects of industry than those who are affluent. As communities of color have organized to confront this problem, their claims of injustice have revealed significant issues across all sectors of environmental governance, both in the U.S. and internationally, and reflect failures of representative institutions in urban land management. In this manuscript, we derive a socio-spatial approach to management of facility siting decisions based on the research in environmental justice. Then we discuss some reforms to facility siting that have been proposed and implemented in the U.S., Canada, and western Europe over the course of three decades, and how these reforms can improve the legitimacy of facility siting decisions.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.004 |
| 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".