Household-Level Strategies to Tackle Plastic Waste Pollution in a Transitional Country
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As one of the world’s fastest-growing economies, Vietnam is tackling environmental pollution, particularly plastic waste. This study contributes to the literature on environmental culture and practical solutions by better understanding households’ behaviours and motivations for (i) sorting waste, (ii) contributing to the environmental fund and (iii) relocating. The questionnaire-based interview method was used to randomly collect information from 730 households in 25 provinces in Vietnam during February 2022. Bayesian regression models, coupled with the mindsponge mechanism, were applied to analyse the data. The results showed that people’s strategies and responses to plastic waste pollution vary: 38.63% of respondents were sorting waste at home, 74.25% of households agreed to contribute to the environmental fund, and 23.56% had a plan to relocate for a better living place. The households’ strategies and intentions were driven by several structural and contextual factors such as age of household head, income, care about the environment, and the perceived effects of polluted waste. More importantly, communication was a robust variable in sorting waste decisions, which suggested that better communication would help increase people’s awareness and real actions in reducing plastic waste and ultimately improving the environment. These findings will benefit the ongoing green economy, circular economy, and green growth transition toward more sustainable development, particularly in developing and fast-population-growing countries.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it