What Does Chelsea Creek Do for You? A Relational Approach to Environmental Justice Communication
Bibliographic record
Abstract
Historically, academic and government environmental justice (EJ) research and communication efforts have centered on quantifying, mapping, and visualizing the environmental harms faced by EJ communities (communities facing disproportionate levels of environmental harm). Unangax Education scholar Eve Tuck critiques such frameworks as “damage-centered” because they cast entire communities—predominantly low-income, BIPOC communities—as lacking or lesser. In this case study, we identify three core pitfalls of damage-centered research in government agency EJ projects—reification, obfuscation, and discretization—through our analysis of two important U.S. federal EJ data tools and related policies: the Environmental Protection Agency (EPA)'s EJSCREEN, and the recently unveiled Climate and Economic Justice Screening Tool (CEJST). We center our study on the depiction of the Chelsea Creek Region in Massachusetts. In response, we describe preliminary research on an alternative approach to communicating EJ issues based on a relational rather than damage-centered EJ framework that advances relationships as the fundamental unit of both analysis and redress—in this case the Greater Boston region's relationship to and responsibility for ongoing environmental harms in the Chelsea Creek region.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".