Enhancing Community Understanding of Forest and Land Fire Prevention and Management through Socialization Activities in Musi Banyuasin Regency
Bibliographic record
Abstract
Forest and land fires are recurring environmental problems in Musi Banyuasin Regency, particularly in areas dominated by forest landscapes and peatlands. This community engagement activity aimed to enhance community understanding and awareness of forest and land fire prevention and management through an educational and participatory socialization approach. The activity was conducted in Muara Merang Village, Bayung Lencir Subdistrict, and Pangkalan Bulian Village, Batanghari Leko Subdistrict, involving 45 participants representing village governments, Fire Care Community groups, farmer and forest farmer groups, youth organizations, and women’s groups. The implementation stages included an initial assessment and site selection, coordination with village stakeholders, pre-test administration, delivery of conceptual and technical materials, participatory discussions, and evaluation through post-test. The results indicate that the community’s initial understanding of forest and land fires was relatively adequate, with an average pre-test score of 6.2, although the understanding remained partial. After the socialization activity, participants’ understanding increased significantly, as reflected by an average post-test score of 9.25. Participatory discussions further revealed community needs for institutional strengthening, more intensive training, adequate equipment support, and sustainable land management alternatives without burning practices. These findings highlight the importance of community-based approaches as an initial step in strengthening local preparedness and sustainable forest and land fire prevention at the village level.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".