Developing Education for Sustainable Development-Oriented-Character Learning Model for Indonesian Golden Generation
Bibliographic record
Abstract
This study aimed at developing and investigating the appropriateness of education-oriented-character learning model to develop sustainability to build 2045 Indonesian golden generation. The development model was adapted from Dick and Carey Model with product trial subject to XI graders of some schools in West Nusa Tenggara. The model of character learning was implemented into Biology, Chemistry, and Physics Subjects through learning instrument which had been developed with the syntaxes of character learning model. The indicator of character assessment was adapted from character education indicators which had been combined with 21st Century Skill. The data collection instruments included self assessment and observation sheets. The findings showed that: (1) the developed character learning model had syntaxes of collect, discuss, analyze, communicate, and apply; the problem and the solution was focused on themes of environment, society, and economy; (2) education for sustainable development-oriented-character learning model which had been developed was worth applying with very good category.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".