LARANGAN SEBAGAI MEDIA PELESTARIAN HUTAN PADA KOMUNITAS ADAT CIKONDANG
Bibliographic record
Abstract
This research is conducted because of the frequent forest destruction occurrence, meanwhile, the forests in the Cikondang Indigenous Community area are still well-preserved to date. This study aims to obtain an overview of the sacred grove located in Cikondang, the reason why the forest is designated as a sacred grove, and the factors that cause the forest to be well-preserved. This research is a descriptive one by exercising the qualitative method. The qualitative method is used to analyze objects that cannot be measured using numbers. The research methods were conducted by literature studies, observations, and interviews. Informants, both key and additional, were interviewed with reference to interview guidelines. The research stage is initiated by extracting the data in the field, classifying and analyzing the data, and finally drawing a conclusion. The results indicated that the forests in Cikondang physically have similarities with forests in general. Historical events and awareness of the benefits of the forest designated the forest as a sacred grove whose preservation has been well-maintained due to the enactment of a number of forbiddance or pamali related to the existence of the forest. Forbiddance or pamali is a customary rule that is obeyed because of fear of violating the forbiddance or pamali. It has the "power" to prevent people from causing forest destruction. Forbiddance or pamali can be a means to preserve the forest.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.022 | 0.001 |
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".