Infrastructuring Care: Co-Designing Computational Notebooks for Environmental Data Justice
Bibliographic record
Abstract
The 2016 election of Donald Trump heightened the disproportionate risks from toxicants faced by minoritized communities from cuts in the US Environmental Protection Agency (EPA) funding, reworking environmental health regulations, and obfuscating related information. In response, the Environmental Data and Governance Initiative (EDGI)—a US-based network of social scientists, technologists, and activists—was formed to develop an environmental data justice framework to address the ongoing political forces that influence how environmental data is collected, shared, and rendered flawed, incomplete, or vulnerable. By collaborating with communities and non-profit organizations, EDGI's Environmental Enforcement Watch (EEW) project designed computational notebooks that critically situate, question, and analyze EPA datasets on industry compliance and state enforcement of environmental regulations beyond the Trump political moment. These civic partnerships expose gaps, biases, and silences in environmental data that sustain a harmful permission-to-pollute system. Using a multi-sited auto-technography, we reflect on the development and use of EEW notebooks, tracing their application across workshops, collectively designed reports, and data stewardship practices. We show how feminist care practices informed the creation of computational notebooks as an effective resource for environmental justice groups to negotiate environmental datafication, supporting the co-production of critical knowledge about environmental governance.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".