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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".