Plastic Legacies: Pollution, Persistence, and Politics
Bibliographic record
Abstract
In Chapter 11, "Plastics Talk/Talking Plastics: The Communicative Power of Plasticity," Deirdre McKay and her colleagues outline insights gleaned from two action research projects conducted in the Philippines and United Kingdom.They explore how plastics communicate important messages about class, gender, and identity formation of the subaltern.The authors outline how global attempts to respond to plastics pollution often neglect the socio-cultural differences between social groups, the important messages that plastics "speak" to us about including gender and Indigenous politics, and the cultural subversion of global waste.The authors emphasize that we need to learn to be attentive to the messages that plastics communicate if we are to respond to plastics pollution in empowering and meaningful ways.Chapter 12, the final chapter, from Trisia Farrelly, Ian Shaw, and John Holland, is entitled "Redressing the Faustian Bargains of Plastics Economies."The chapter outlines the need for a legally binding international treaty on plastics pollution based on the successes of the Montréal Protocol on chlorofluorocarbons (CFCs) and other ozone-depleting substances.The authors emphasize that such a global instrument must focus on prevention and the full life cycle of plastics from extraction to recovery of legacy plastics.The chapter foregrounds the hazardous, ambiguous, and unpredictable nature of plastics' physical, toxic, and biological entanglements and consequently argues that legislation should focus on preventing
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".