Household Participations and Sustainable Development Programs: Social Impact of Government Assistance in Indonesia
Bibliographic record
Abstract
This study aims to evaluate the impact of government funding on household members' participation in social activities.The data used in this study comes from the publication of the Indonesia Family Life Survey (IFLS) which contains information on aspects of household life in Indonesia, including social activities The main problem in household economic analysis is too many determinants of social activity at both the household and community levels.As a result, there are many confounding factors at the household and community level.Therefore, to isolate the effect of un-observed heterogeneity at the household level, this study uses a First-Difference approach.Meanwhile, to overcome the possibility of bias at the community level, this study uses a community-level Fixed-Effect approach.The combination of First Difference (FD) and Fixed Effect (FE) to isolate various external determinants in the model is an important innovation in this research.The results of the study show that households that receive government assistance are more involved in social activities.For this reason, appropriate government assistance can be used to increase community participation in development.This research in the future can still be developed by expanding the scope of social activities analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".