Re-Adaptation of COVID-19 Impact for Sustainable Improvement of Indonesian Villages' Social Resilience in the Digital Era
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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