Linking Health Inequality and Environmental Justice: Articulating a Precautionary Framework for Research and Action
Bibliographic record
Abstract
This article draws together three issues—the environment, health, and (in)justice—with the overall purpose of articulating an agenda for policy and research that works towards improved justice and sustainability in the environmental health arena. Considerable research in the United States and elsewhere has shown that both environmental exposures and poor health are more prevalent in populations that are marginalized by race and social class (typically measured as income). The logical next step has been to attempt to establish concrete cause-effect links between health effects and environmental exposures in order to mobilize government action to reduce these disparities. However, we caution against pursuing such causal links alone as a necessary precondition for just and sustainable environmental health policy. We instead argue for a framework that considers both environmental justice and health inequality in terms of compounded disadvantage at the community level. We support a precautionary approach to action that simultaneously pays due attention to the processes leading to injustices/inequities as well as remediating current patterns of injustice/inequity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.102 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.016 | 0.152 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".