Strategi Percepatan Penghapusan Kemiskinan Ekstrem di Provinsi Sulawesi Tengah: Pendekatan Konvergensi dan Inovasi Program
Bibliographic record
Abstract
Extreme poverty in Central Sulawesi remains a significant challenge that demands an integrated strategy. This study evaluates the effectiveness of government programs, including Gercep Gaskan Berdaya and PADUNGKU, in accelerating the eradication of extreme poverty. Secondary data from the 2023 P3KE Update and the 2024 BPS were analyzed using multiple linear regression. The findings reveal a 0.64% reduction in extreme poverty during the 2023-2024 period, with notable declines in Donggala (0.95%) and Poso (0.93%). The "Gercep Gaskan Berdaya" program, which combines cash assistance and entrepreneurial training, successfully increased the income of impoverished households; however, aid distribution often faced challenges in areas with limited access. Despite these efforts, Parigi Moutong and Poso districts still have the highest numbers of people living in extreme poverty, indicating a need for program intensification. While existing programs have shown positive outcomes, long-term success heavily depends on improving infrastructure access, enhancing cross-sectoral coordination, and providing better basic services. The convergence and innovation of socio-economic programs, along with improved infrastructure access, have proven effective, although challenges persist in remote areas. A comprehensive approach and enhanced coordination are required to meet the target of eradicating extreme poverty by 2024.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".