UPAYA PEMBINAAN PEMERINTAH KABUPATEN BANYUWANGI DALAM MENANGGULANGI KAUM MISKIN KOTA DI KECAMATAN BANYUWANGI
Bibliographic record
Abstract
The issues in this research are related to the high poverty rate in the Banyuwangi District, particularly experienced by street vendors, buskers, and street children.The existence of this group reflects a complex social challenge that demands serious attention and handling from the local government.The purpose of the research is to analyze the coaching efforts undertaken by the Banyuwangi Regency Government in addressing the issues faced by the urban poor.The research also identifies the obstacles that arise during the implementation of community welfare improvement programs.The methodology used was a descriptive qualitative approach.Data collection techniques were carried out through in-depth interviews with key informants and direct field observations.Data analysis includes the processes of reduction, presentation, and conclusion drawing to obtain an objective and comprehensive picture.The research results show that government programs have not been fully effective in addressing poverty.The low level of public awareness and the limited capacity of human resources are the main obstacles in the implementation of policies.The community feels that the services provided do not meet their expectations.The conclusion of this research underscores the importance of enhancing the capacity of bureaucratic resources and improving program governance.Strengthening communication between the government and the community is also necessary to ensure that policies are more targeted.More effective implementation is expected to drive sustainable improvement in community welfare.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".