KARAKTERISTIK SOSIAL YANG MEMPENGARUHI PERSEPSI DAN PERILAKU MASYARAKAT DALAM PENGELOLAAN HUTAN KEMASYARAKATAN
Bibliographic record
Abstract
The management area of KPH VIII Batu Tegi which is a watershed of the Way Sekampung watershed included in the priority watershed category because most of the watershed areas have experienced changes in forest function. So that all forms of land management in the region can affect the quality and quantity of the Way Sekampung watershed, including a Social Forestry scheme with community empowerment. Community empowerment in KPH Unit VIII Batu Tegi needs to take into account to aspects of community characteristics that affect its perception and behavior in forest management. This study aimed to analyze the characteristics that influence people's perception and behavior in managing HKm. Respondents in this study were 71 members of the Mandiri Lestari Forest Farmers Group (Gapoktan) who have working areas in the Protected Forest area register 39 Kota Agung Utara. The analytical method used is non parametric statistical correlation Spearman Rank. The instrument used is a Likert scale. The results obtained indicate that the Social characteristics that have a real influence on people's perceptions are age. While the level of community behavior is not influenced by the observed characteristics of the respondents. Keywords : behavior; characteristics; perception, community forest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".