Shifting Cultivation System of Indigenous Moronene as Forest Conservation on Local Wisdom Principles in Indonesia
Bibliographic record
Abstract
This research is a case study conducted in the village of Indigenous Moronene Huka'ea - La'ea, Watu-Watu village Lantari Jaya sub-district, Bombana. The study followed a series of processes and stages of work in the agriculture system based on local wisdom of Moronene tribe, as one of the patterns of forest conservation. This study applied a "descriptive-qualitative", to describe the social and behavioral conditions of indigenous peoples in managing and utilizing forest resources around the neighborhood where they live. The results of this study indicate that the indigenous of Moronene form of traditional knowledge - local and skills to manage forests for agricultural fields, is quite effective in guaranteeing the sustainability of the forest around the area. One of the local wisdom related to forest management is ancestral policy to regulate the system of grouping the forest area into four zones, including: Inalahipue (rainforest), Inalahi Popalia (sacred forest), Inombo (production forest), and Lueno (forest /wildlife habitat). The practices of shifting cultivation occur in the Inombo forest areas from generation to generations of Moronene in the in the village as the main livelihood systems.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".