The Effect of Rubbish Management Socialization Based on Indonesian Ulama Council’S Fatwa Number 47 of 2014 on Community Behavior in Dealing with Rubbish Problem
Bibliographic record
Abstract
Rubbish has been an acute and less resolvable problem, because the volume of rubbish produced by human being increases over years. Many attempts have been taken to solve this problem, one of which is through the economic value-based socialization of rubbish management. However, the result has not been able to generate consciousness and to change the community behavior collectively in dealing with rubbish problem. This research studied another attempt taken to deal with rubbish problem, the socialization of rubbish management using economic and religion value combination. This research was conducted in Sleman Regency. Data was collected through interview, observation, and documentation. Analysis was conducted interactively. The result of research showed that the socialization of rubbish management based on the Indonesian Ulama Council’s Fatwa Number 47 of 2014 could build consciousness and change the community behavior collectively in dealing with rubbish problem, when the participants selected reflected on the representativeness of all community elements, the presence of volunteers from the community who were willing to organize, the participants were given information completely about the objective of socialization, government’s support to the community conducting rubbish management activity, and the material of socialization was taken for granted by the participants of socialization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".