Identifikasi Kegiatan Program Kampung Keluarga Berencana di Kabupaten Aceh Tengah
Bibliographic record
Abstract
The purpose of this study identified the activities of the Family Planning Village Population, Family Planning and Family Development program in Middle Aceh District. The study location were grouped according to the Geographic Difficulties Index of the Central Bureau of Statistics. Research data collection uses qualitative methods, namely interviews, documentation, and observations with the Village Head / Geuchik (informant 1), extension / coach of the Family Planning Village (informant 2), and management of the Family Planning Village (informant 3). The results of this study indicate that the implementation of family planning village activities in the village is of high difficulty and is not difficult geographically has been relatively active, namely the management and mentor and companion of the family planning village to work well and directed, while the villages with moderate difficulty level geographically less active. Generally, the activities of the Family Planning Village carried out are posyandu, posbindu, elderly gymnastics and other activities. The activities of the Prosperous Family Income Improvement Group are also not yet implemented, where the activity is expected to empower the village community economically. Cross-sectoral activities have also not been implemented and coordinated well, it seems that there is no program that can be felt directly even though the commitment at the beginning of the declaration has been carried out.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".