Incremental progress or dangerous incrementalism? The case of tire wear pollution in global environmental governance
Bibliographic record
Abstract
Tire wear and tear is among the largest sources of global microplastic pollution. Interviews conducted in 2024 appear to indicate ‘incremental progress’ toward improving governance of tire wear. Knowledge of the ecological and health consequences is increasing. Pressure is growing for greater producer responsibility. Global standards to limit tire abrasion are forming. Regulations are being implemented to address chemical contamination, such as in California. And some manufacturers are supporting higher standards and reengineering tires to undercut competitors and capture emerging markets for lower-abrasion tires. Yet, as a deeper analysis reveals, regulations remain highly uneven, piecemeal, and inadequate on a global scale, with new risk-taking as firms delay actions and introduce new chemicals as ‘solutions.’ Moreover, I argue, a ‘dangerous form of incrementalism’ is taking hold, where modest changes to state policy and corporate conduct are conferring legitimacy on governance processes unable to prevent tire wear pollution from escalating globally.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".