Plastic Pulse of the Public: A review of survey-based research on how people use plastic
Bibliographic record
Abstract
Abstract Plastics pollute all environmental compartments because of human activities and mismanagement. Public perceptions and knowledge about plastic pollution differ among individuals and across different jurisdictions. Targeted survey-based research tools can help measure consumer awareness about the impacts of mismanaged plastics and help identify trends and solutions to reduce plastic use and plastic pollution. This review primarily focused on survey-based research from presenters at the scientific track session TS-2.15 Plastic Pulse of the Public at the 7th International Marine Debris Conference ( www.7imdc.org ) and supplemented by contemporary literature. Survey-based research helps provide new insights about public opinions related to the pervasiveness of plastic pollution. This review includes results about consumer use and perceptions of plastic pollution impacts from diverse studies from nine countries including Ghana, Kenya, Bangladesh, Pakistan, United States, Canada, Norway, Germany, and United Kingdom. Overwhelmingly, public perceptions and consumer awareness of the negative impacts of plastic pollution were extremely high, regardless of geographic location. Awareness about the environmental impacts of plastic waste and plastic pollution was highest within younger, white, female, and well-educated demographic groups. However, differences were observed in public attitudes toward willingness to pay for sustainable alternatives, end-of-life plastic uses, unintended consequences, recycling, and mismanagement.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".