Practicing environmental data justice: From DataRescue to Data Together
Bibliographic record
Abstract
The Environmental Data and Governance Initiative (EDGI) formed in response to the 2016 US elections and the resulting political shifts which created widespread public concern about the future integrity of US environmental agencies and policy. As a distributed, consensus‐based organisation, EDGI has worked to document, contextualise, and analyse changes to environmental data and governance practices in the US. One project EDGI has undertaken is the grassroots archiving of government environmental data sets through our involvement with the DataRescue movement. However, over the past year, our focus has shifted from saving environmental data to a broader project of rethinking the infrastructures required for community stewardship of data: Data Together. Through this project, EDGI seeks to make data more accessible and environmental decision‐making more accountable through new social and technical infrastructures. The shift from DataRescue to Data Together exemplifies EDGI's ongoing attempts to put an “environmental data justice” prioritising community self‐determination into practice. By drawing on environmental justice, critical GIS, critical data studies, and emerging data justice scholarship, EDGI hopes to inform our ongoing engagement in projects that seek to enact alternative futures for data stewardship.
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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 | 0.206 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.036 | 0.135 |
| Scholarly communication | 0.056 | 0.085 |
| Open science | 0.006 | 0.072 |
| Research integrity | 0.016 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".