Re-Adaptation of COVID-19 Impact for Sustainable Improvement of Indonesian Villages' Social Resilience in the Digital Era
Bibliographic record
Abstract
The policy article aims to formulate a re-adaptation to the impact of COVID-19 that strengthens the social resilience of villages in a sustainable manner based on empirical findings. This article uses a sequential mixed model research design approach analysis with focus group discussion, and it is strengthened by data collected from 105 respondents chosen through random sampling techniques, online in-depth interviews, and group interviews in the villages where the article was conducted. The result showed that the village government was able to build a dialogue with villagers to find common understanding and build collective action to overcome the impact of COVID-19. Another finding is that the village government can realize real action in synergizing social protection policies from the government with the development of social security in rural communities. It was concluded that the experience of overcoming the impact of COVID-19 should be used as an innovation in the development mechanism of village governments in Indonesia. The innovation described in this article is known as re-adaptation. Disaster adaptation is designed and included in the village government's development planning mechanism document. The article has limitations because it does not examine existing regulations that could be used to expand innovative practices.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".