DAMPAK PERUBAHAN MODAL SOSIAL TERHADAP PERUBAHAN SISTEM PENGOLAHAN SAMPAH DI YOGYAKARTA
Bibliographic record
Abstract
Badran village is a village that used to have a reputation as thugs village and face the problem of garbage. Over time, this village managed to overcome the problem of garbage with 3R-based waste management program. This success is motivated by changes in social capital and leadership as a catalyst in the presence of co-operation. Forms of social capital that result in changes in the waste management system RW 11 Kampung Badran is trust, norms and networks. The third form of social capital and the existence of aspects of leadership affects the sustainability of programs aimed at managing waste in RW 11, Kampung Badran. For that we need further review of how the influence of the three components of social capital that is trust, norms and networks of the waste management system. The method used the descriptive qualitative method with the presentation of narrative technique. Based on the method applied was found that all three forms of social capital affects the implementation of waste management activities in the form of bank program garbage, composting and manufacture craft of garbage. Trust influence the process of knowledge transfer of cadres to the citizens, norms affect the implementation of programs and networks organizing effect on the acquisition of outside resources. Keywords: social capital, trust, norms, network, 3R
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".