PEMBERDAYAAN MASYARAKAT DALAM PENGELOLAAN SAMPAH SEBAGAI UPAYA PENANGANAN KAWASAN KUMUH DI KAWASAN PECANGAAN KABUPATEN JEPARA
Bibliographic record
Abstract
Increased urbanization and the increase in the number of housing areas or settlements that are less organized, accompanied by increasing slums. Slum settlement is defined as a residential area that is unfit for habitation with irregular building conditions, has a high level of building density, with the quality of buildings and facilities and infrastructure that do not meet the requirements. The problem of this research is the low coverage of solid waste services and the increasing volume of non-organic waste that is difficult to recycle and the level of public awareness about cleanliness is the main problem of waste management. The purpose of this research is to solve the problem of solid waste as an effort to prevent settlements from slums and to create a community that cares about waste management. The conclusion of this research is the need for regular waste transportation services to TPS / TPA so that the Pecangaan area becomes cleaner, the procurement of trash bins for each RT so that the waste can be well accommodated and the infrastructure for the process of transporting waste, socializing to the community at community meetings about the importance of waste management that is carried out in a sustainable manner, providing incentives for communities / groups / areas that are able to manage their waste properly as pilot areas, providing training to residents on waste management with the 3R concept (Reuse, Reduce, Recycle).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".