What Does Chelsea Creek Do for You? A Relational Approach to Environmental Justice Communication
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it