Waste Segregation at Source: A Strategy to Reduce Waterlogging in Sylhet
Bibliographic record
Abstract
Poor solid wasteSolid Waste-management systems in cities in developing countries make them vulnerable to climate-induced risksClimate-induced risk. It has been pointed out in the literature that the waste management process needs to be holistic and inclusive from waste generation to disposal in order to make it efficient and sustainable. While women in their day-to-day activities at home play a critical role in waste management, they are often excluded in the public waste-management systems which are mainly managed by men. This research used women-centric approaches for motivatingMotivating citizensCitizens using social and moral persuasion, economic incentives and social recognition to participate in municipal solid waste managementMunicipal solid waste management. The findings indicate that the awareness campaign using motivational approaches eventually worked and that the women-centric approaches used are important for promoting home-based waste segregationSegregation at source. The study also revealed that a simple payment mechanism for waste disposal services at the householdHouseholds level is not enough to convert littered cities into clean cities. A women-centric approach also contributes to developing community-based solutions to adapt to climate-induced flooding and makes a city more resilient, addressing sustainable development goalsSustainable Development Goals (SDGs).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".