Dampak Alokasi Dana Desa (ADD) Terhadap Pengembangan Ekonomi Di Kecamatan Ajibata Kabupaten Toba Samosir
Bibliographic record
Abstract
Kabupaten Toba Samosir merupakan salah satu kabupaten yang ada di Provinsi Sumatera Utara. Kabupaten Toba Samosir terdiri dari 14 kecamatan, 192 desa / kelurahan, yang responsif terhadap tuntutan desa. Kabupaten Toba Samosir telah mengalokasikan dana untuk desa dengan harapan pembangunan semakin merata sampai ke tingkat desa. Salah satu wilayah Kabupaten Toba Samosir yang memperoleh alokasi dana desa adalah Kecamatan Ajibata yang merupakan ibukota Kabupaten Toba Samosir. Penelitian ini bertujuan untuk menganalisis perencanaan, pelaksanaan, evaluasi dan pertanggungjawaban ADD, serta dampak Alokasi Dana Desa terhadap pengembangan ekonomi di Kecamatan Ajibata Kabupaten Toba Samosir. Metode penelitian menggunakan analisa deskriptif dan uji beda rata - rata. Hasil penelitian menunjukkan bahwa Kebijakan Program Alokasi Dana Desa (ADD) di Kecamatan Ajibata Kabupaten Toba Samosir berjalan cukup lancar. Hal ini dapat terlihat dari tahap persiapan berupa penyusunan Daftar Usulan Rencana kegiatan (DURK), pelaksanaan setiap kegiatan, evaluasi kegiatan sampai dengan tahap penyusunan pertanggungjawaban. Pendapatan masayarakat Kecamatan Ajibata meningkat setelah adanya program ADD
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".