Improvement of Waste Management Through Community Awareness of Plastic Controlling in Garang Watershed, Semarang City, Indonesia
Bibliographic record
Abstract
This study aims to analyze the condition and awareness of community plastic waste management in the Garang watershed to increase institutional capacity in reducing plastic pollution. This study was a sequential exploratory mixed-method research involving 175 respondents from Garang rivers community. Data was collected using observation, open-questionnaire and in-depth interview about community understanding and waste management organization. The respondent answer then converted into number and analyzed statistically using Kruskal-Wallis test. The institutional aspect was identified by interview and scored for AHP analysis. This research predicts more than 66 ton of plastic waste was produced by the communities around Garang watershed that managed, inappropriately. Only less than 40% of the Garang watershed community sells their plastic waste to the waste bank, and the rest were burned or abandoned in vacant land or rivers. Regarding to the waste-management organization aspect, the financial support and community participation aspect should be improved in upstream wather-shed area to enhance waste management communally. In contrast, internal institutions, community participation, and operational institutions are the main aspects that might be enhanced in the downstream areas. A future research needs to be conducted to identify community requirements as a foundation for establishing an appropriate waste management institution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".