PENYALURAN BANTUAN LANGSUNG TUNAI DANA DESA (BLT-DD) DIMASA PANDEMI COVID-19 DI DESA PEWISOA JAYA KABUPATEN KOLAKA
Bibliographic record
Abstract
This study aims to determine the practice of distributing the Village Fund Direct Cash Assistance (BLT-DD) program during the Covid-19 Pandemic in Pewisoa Jaya Village, Tanggetada District, Kolaka Regency. This research is a qualitative descriptive study with data collection methods through interviews, observation and documentation. The results of this study indicate that the implementation of the BLT-DD program during the Covid-19 Pandemic in Pewisoa Jaya Village, Tanggetada District, Kolaka Regency, has not gone well, this can be seen from the determination of the names of Family Cards (KK) as BLT-DD recipients that are not correct. target. In this case, in Pewisoa Jaya Village, there was never any updating of the Integrated Social Welfare Data (DTKS) even though they visited people's homes to collect community data as potential recipients of BLT-DD. The achievement of the objectives of the program policy in Pewisoa Jaya Village has not run optimally, because there are still people who are in the capable category and have received other social assistance but whose names are registered again as BLT-DD recipients, while there are still many poor people who have never been touched by social assistance. so that the objectives of the BLT-DD program have not been fully targeted.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".