Model Pengembangan Ketahanan Pangan Berbasis Pisang Melalui Revitalisasi Nilai Kearifan Lokal
Bibliographic record
Abstract
This research aims to find” Food Endurance Development Model Based on Banana with Revitalisation Local Wisdom Value Reinforcement” . This model serve the purpose of basis for formulate public policy and education efforts and advocation public in the field of food to push national food endurance. Approach research that used in this qualitative research with qualitative descriptive design. Subject research is bapeda, development, agriculture official, farmer, elite figure, farmer group at regency Lumajang, Malang, and Blitar. Technique sampling that used snowball sampling. Data collecting method that used documentation, indepth interview, observation participatory, and limited discussion. Research data that got to analyzed with qualitative analysis (content analysis, and domain analysis). Based on research result inferential: 1) found banana production profile unity, distribution, consumption, and local wisdom character at regency Lumajang, Malang, and Blitar, 2) local wisdom character can be made principal focus in the effort develop food endurance based on banana, and 3) several important components and strategic of food endurance development model based on banana: a) local wisdom (foodstuff use reinforcement based on local, woman character, society/religion figure character, food self-supporting village, environment friendly agriculture, agriculture multiculture, and planning based on society), b) local government character (wisdom development prima tani, pilot projecting, capitalization, assistance, and tool of productions-distributions-marketing-consumption), and c) and character BPTP, BBMP, DUDI (pilot development projecting, capitalization, assistance, and system reinforcement productions-distributions-marketing-consumption).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".