PENGGUNAAN BUKU BERGAMBAR TENTANG CARA BETERNAK DOMBA UNTUK MENGEMBANGKAN KAPASITAS PETANI MISKIN DI DUA DESA TEPI HUTAN DI KECAMATAN UJUNG JAYA, KABUPATEN SUMEDANG, JAWA BARAT
Bibliographic record
Abstract
Building the capacity of the poor farmers that were living in two near-forest villages in Ujung Jaya sub-district, District of Sumedang, West Java was one of the activities accomplished to help such farmers raise indigenous Garut sheep. These activities included on site farmer training, monthly farmer meeting, supervision and other backstopping supports. These support services were provided before and after the sheep provision to the cooperating farmers. In this training, copies of an illustrated sheep raising manual covering several aspects of sheep rearing from feeding to marketing were tested with the participating farmers and were later given to them as reading materials. In this conjunction, two farmer groups from Palasari and Sukamulya villages were involved in the training sessions. At the opening session, the two farmer groups were tested to determine their prior knowledge about sheep rearing. After the pretetst, all participating farmers were individually given a copy of the sheep raising manual and was asked to read the manual for about 25 minutes. Next, they were asked to close the books and were given posttests. The results pointed out that the participating farmers improved their knowledge about sheep raising, though statistically not significant. Other tests accomplished on the data obtained, showed that the participating farmers’ education correlated positively with their knowledge gains regarding sheep raising; whereas their ages and number of years since graduation correlated negatively with their knowledge gains about sheep raising.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".