Confronting Chronic Pollution: A Socio-Legal Analysis of Risk and Precaution
Bibliographic record
Abstract
The central aim of this article is to demonstrate a socio-legal approach to risk and precaution using the example of chronic pollution. Drawing on ongoing empirical work with the Aamjiwnaang First Nation, which is tucked into Sarnia's "Chemical Valley," a secondary aim is to influence and shape how we understand the problem and confront the risks of chronic pollution. This article forwards the argument that the prevailing regulatory approach is incapable of capturing the essence of contemporary pollution harms, because those harms are increasingly linked to continuous, routine, low-dose exposures to contaminants that are within legally sanctioned limits. Community residents and advocates struggling against chronic pollution are increasingly identifying with the environmental justice movement and adopting its strategies of resistance, including its mantra of "precaution." These strategies of resistance have the potential to dramatically expose the impotence of the prevailing regulatory approach to chronic pollution.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.081 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".