Bibliographic record
Abstract
The diversity of the language of the environment of Green School Bali needs to be studied, especially the treasury of green ecolexicon as they reflect strategical effort to preserve local wisdom of Balinese culture. This study used a descriptive qualitative approach. Data was obtained from the Green School Bali educational environment by using observation and interviews method. The results shown that the grammatical category of the ‘green’ lexicon is in the form of nouns and verbs that are in the form of basic words and phrases and the ‘green’ syntactic construction at Green School Bali contains these natural lexicons, including noun phrases such as bambu hitam ‘black bamboo’, and verb phrases such as bermain Jegog ‘play Jegog’ while the social praxis dimension of the green ecolexicon namely the ideological dimension, the sociological dimension and the biological dimension. This research also uniquely contributes to preserving the concept of local wisdom in real action in the context of international education in Bali.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".