Uncertainties about waste using an online survey and review approach: Environmentalist perceptions, household waste compositions and views from media and science
Bibliographic record
Abstract
Abstract Waste generation and subsequent plastic pollution pose a major threat to both human and environmental health. Furthering our understanding of waste at individual levels can inform future waste reduction strategies, education and policies. This study explores the components and perceptions among individuals using survey data combined with a mini-review. An online Qualtrics survey was distributed pre-COVID-19 following a global social media challenge, Futuristic February, which directed participants to collect their nonperishable waste during February 2020. Participants were asked about their waste generation, perceptions toward waste and plastic pollution issues, and environmental worldview using the New Ecological Paradigm (NEP) scale (n = 50). We also conducted a mini-review of eight waste and plastic pollution statements from our survey in both popular media and scientific journal articles. Survey results indicated participants had an overall pro-ecological worldview ( M = 4.32, SD = 0.88) and reported cardboard and paper (66%) as the most commonly occurring nonperishable waste category. Across categories, food packaging was the most common waste type. Participants were most uncertain about statements focusing on bioplastic or biodegradable plastic, respectively (44% and 30%), while the statement on microplastic toxicity obtained 100% mild or strong agreement among participants. Uncertainty for reviewed statements varied depending on the topic and group. Popular media and scholarly articles did not always agree, possibly due to differences in communication of uncertainty or terminology definitions. These results can inform future policy and educational campaigns around topics of misinformation.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".