Refusing to Die: Programmatic Goods in the Fight against COVID-19 in Sampang Regency
Bibliographic record
Abstract
This article discusses programmatic distributive politics in the villages in Sampang Regency during the COVID-19 pandemic. This study seeks to answer the forms of programmatic goods distributed in Sampang during the pandemic. This study employs qualitative methods and chose ten villages in Sampang as its case study due to Sampang’s achievement in maintaining its green zone status for the longest period in East Java. This article shows that there have been shifts in the bupati’s relationships with the village heads, from what was previously transactional prior to the pandemic to be more collaborative in efforts to contain the spread of the virus. This study also finds that the practice of distributive politics in Sampang during the pandemic fulfills the three criteria of programmatic politics: the accuracy of beneficiaries, transparency, and commitment to distribute goods without discrimination. The village heads in Sampang have acted as effective brokers in the implementation of village welfare programs, such as the installment of village volunteer posts against COVID-19, the free mask program for villagers, the BLT-Village Fund (BLT-DD) scheme targeting villagers from low-income households affected by the pandemic, the distribution of staple foods (sembako), the smart village program that provides free internet access in every village.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".