Chapter 13 Household waste management and the role of gender in Nepal
Bibliographic record
Abstract
Solid waste management has become one of the most important issues in urban centres of developing countries where population growth puts pressure on public services. Nepal is struggling to manage municipal solid waste in urban centres due to a lack of segregation at the source, recycling, and proper disposal. This chapter examines whether women and men manage household waste differently at the household level, especially at source segregation, managing recyclable waste (paper and plastic), and composting degradable waste. Using household survey data from the Bharatpur Metropolitan City of Nepal, we find that women are more likely to segregate waste at the source and also manage degradable waste at home better. Still, there is no gender difference in selling plastic and paper waste. In contrast, women are more likely to give paper or plastic waste either to the waste collectors (free) or throw away, suggesting a heterogeneity across gender when it comes to managing household waste. In most cases, women waste managers perform well (segregating at source and composting degradable waste), but they do not seem to do well in all areas of plastic or paper waste management where some sort of sensitization may be helpful.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".