Model Piranti Olah Pikir-Emosi untuk Menumbuh-kembangkan Cinta Budaya Bangsa Siswa Sekolah Dasar
Bibliographic record
Abstract
This article is to create thought-emotion processing device model in theform of CD that is ready to be applied by teachers in order to to develop of love towardnational culture of elementary school students. Short-term goal of drafting a model in theform of CD as well as information on the effect of the application of psycho-educationalthought-emotion processing device toward love of national. In the first year, designingpsycho education models thought-emotion processing device that can increase primarystudents’ character of love toward national culture. In the second year, the researcherconduct experiment activity to test the effect of thought-emotion processing device modelof national culture love in 3 areas that could represent primary student characteristicin Indonesia. This research is usefull for teacher to implement character-based learningcomprehensively in the classical style in order to increase the character of love towardnational culture. The usage is teachers can implement by themselves with a manner andsteps which is recommended in learning guide based on this character education.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".