Factors Influencing Residents Dealing with Municipal Organic Waste in Developing Countries: Evidence from Rural Areas of Hoi An, Vietnam
Bibliographic record
Abstract
Municipal solid waste (MSW) management in developing countries is facing many challenges. Most MSW is disposed of in landfill areas that are uncontrolled and overloaded. Under budget constraints, the governments encourage residents in rural areas to treat municipal organic waste (MOW) by themselves. Evidence from Hoi An, Vietnam has shown the potential for residents practicing MOW treatment at the source which may divert large quantities of biodegradable waste away from landfills. Logistic regression analysis showed that various factors influence residents treating MOW by themselves, including the gender of the person in charge of waste management, household size, presence of garden, type of city collection methods, care of collectors, and participation in compost training. Several factors are largely insignificant, including age, household income, number of children less than 15 years old, schooling years, and potential for high collection fees in the near future. The results further suggested that local authorities should encourage residents to practice MOW treatment at the source by enhancing the role of local women’ groups, gardening clubs, composting training classes, and paying attention to communal collectors’ roles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".