Model Pengembangan Ketahanan Pangan Berbasis Pisang Melalui Revitalisasi Nilai Kearifan Lokal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it