Public perceptions and expert opinions about microplastic and nanoplastic contamination in water
Bibliographic record
Abstract
Abstract Plastics, including microplastics and nanoplastics, pose significant environmental and health concerns. These particles are found in various environments, particularly water bodies, encouraging governments to implement plastic bans. Public risk perception and expert opinions are crucial in policymaking. However, previous qualitative research exploring public and stakeholder perspectives on this pollution is inadequate. This study aims to qualitatively explore public perceptions of microplastic and nanoplastic pollution by analyzing comments from seven Reddit posts and conducting semi-structured interviews with ten experts from different Canadian provinces and sectors. The risk perception model developed by van der Linden, a social and psychological framework, guided the analysis. A total of 781 Reddit comments were analyzed, and five themes reflecting public perceptions were identified. The results indicated a public awareness gap, efforts to avoid plastics, and distrust in governments, scientists, and the media. While the public shares concerns with experts, there are differences and gaps in public understanding of the pollution. Enhanced risk communication and education are essential for raising awareness and encouraging plastic avoidance, which can lead to more effective policymaking. Further research on plastic management is necessary to guide policymaking, as stakeholders have highlighted gaps in current policies. The findings will inform policymaking and health promotion programs aimed at better managing plastics and educating the public about plastic pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".