"Building Bone Forward: Collaborative Strategies to Accelerate the Human Development Index"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Improving the Human Development Index (HDI) is a strategic step toward promoting sustainable and equitable development. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aimed to accelerate human development achievements by analyzing the constraints and challenges in meeting HDI targets and by providing recommendations to support sustainable human development in Bone Regency. The data used in this analysis consisted of HDI composite indicators covering health, education, and economic dimensions, including life expectancy, mean years of schooling, expected years of schooling, and adjusted per capita expenditure. These data were obtained from the Central Bureau of Statistics (BPS) of Bone Regency for the year 2024. Data collection was conducted using documentation techniques, while data analysis employed descriptive statistical methods. The results indicate a consistent improvement in HDI from 2010 to 2023; however, significant challenges remain, particularly in the education dimension and the standard of living. Recommendations to accelerate HDI improvement in Bone Regency include mapping the achievement of composite indicators and sub-indicators, providing comprehensive assistance for underperforming indicators, enhancing the quality of education, improving health services, and optimizing regional fiscal capacity to support future human development initiatives. The implications of this community service activity highlight that collaboration among local governments, educational institutions, and the community is a key factor in accelerating improvements in quality of life.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it