Impact of an Infrastructure Development Policy on Health, Poverty & Crime Actions in Indonesia (Case Study in Majalengka District)
Bibliographic record
Abstract
With the construction of toll roads, the welfare of the people in the area has also changed. Toll road infrastructure plays a very important role in supporting the economy, social, culture, unity, and dynamics of community life. Majalengka Regency is one of the areas affected by the construction of the Cipali Toll Road (Cikampek-Palimanan). The results showed that in 2014, the life expectancy at birth in Majalengka Regency was only 68.66 years, and in 2019 it had reached 69.97 years. 85.43 percent of households live in their own houses, the remaining 14.57 percent of households live in houses that are not their own. When viewed at a glance, the percentage of own homeownership status in the 2018-2019 period, it can be seen that the percentage of the population who live in their own homes has increased by around 7 percent. The poor population in Majalengka Regency in total showed a downward trend during the 2015-2019 period (a condition in March). In 2015, the number of poor people was 167.50 thousand people or 14.19 percent of the total population of Majalengka Regency. In the 2019 period, the population who became victims of crime continued to experience a decline by 0.72 points to 0.88 percent compared to 2018 which reached 1.60.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".