The power of environmental norms: marine plastic pollution and the politics of microbeads
Bibliographic record
Abstract
Emerging environmental norms gain strength and diffuse more quickly when scientific evidence of harm is consolidating, when activism is intensifying, and when political and corporate resistance is relatively weak. The anti-microbead norm – that plastic microbeads should be removed from personal care products – has been gaining global influence since 2012; witness the upsurge in anti-microbead activism, public concern, voluntary corporate phasedowns and governmental bans. By 2018, the world was on track to eliminate microbeads from ‘rinse-off’ products within a decade, reducing microplastics flowing into oceans by 1–2%. This confirms the power of environmental norms, but how and why this phaseout is occurring – unequally across jurisdictions, with firms creating loopholes, missing deadlines and limiting the scope of reforms – also reveals innate weaknesses of bottom-up, ad hoc norm diffusion as a way of improving marine governance. These weaknesses are heightened when economic stakes are high, solutions are complex and costly, authority is fragmented across jurisdictions and corporate resistance is strong.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".