Community-Based Training as a Solution to Fluctuating Poverty in Sub-districts
Bibliographic record
Abstract
Poverty in urban areas such as the subdistricts (kelurahan) of Buleleng Regency shows significant fluctuations, even though the overall poverty rate has declined. This phenomenon indicates unstable welfare dynamics, particularly among vulnerable groups within the lowest three income deciles. This policy study aims to identify the main causes of poverty fluctuation and formulate relevant policy alternatives for local government implementation. Based on secondary data analysis from BPS and the Coordinating Ministry for Human Development and Culture (Kemenko PMK), along with theoretical reviews, the core issue lies in the instability of household income caused by low human resource competitiveness and the limited provision of government-supported skills training programs. The Human Capital Theory framework is applied to emphasize the importance of investing in human capacity development as a sustainable poverty alleviation strategy, compared to the Infrastructure-Led Development approach, which requires higher costs and extensive land resources. The study proposes three policy alternatives: (1) vocational and entrepreneurship training, (2) participatory community-based training, and (3) training integrated with basic service infrastructure. Using Bardach’s analytical framework—covering technical, economic, political, and administrative feasibility—the participatory community-based training approach emerged as the most viable option. This policy highlights community involvement in designing and implementing training programs aligned with local needs, continuous mentoring, and collaboration with local stakeholders. Its implementation is expected to enhance human resource competitiveness, strengthen community economic independence, and reduce poverty fluctuation sustainably. The study recommends allocating at least 50% of subdistrict funds to community-based training initiatives, supported by measurable indicators of success such as post-training productivity, self-reliance, and community welfare improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".