MODEL PENDIDIKAN KARAKTER KEWIRAUSAHAAN DI SEKOLAH MENENGAH KEJURUAN
Bibliographic record
Abstract
ABSTRACK One of the strengths of Vocational High School compared to the general school is the students trained to be entrepreneur. To support it, the learning model that needs to be developed is the character education for entrepreneurship. The purpose of this research is to gain the appropriate learning model to support character education for entrepreneurship.This study was conducted by using grounded theory approach at Depok 2 Vocational High School, Sleman, Yogyakarta. The reserach subject was chosen by using snowball sampling technique through the setting of place, respondent and activitiy. Data analysis technique was using Miles & Hubberman model. The result of the reserach showed that learning models to enhance character education for entrepreneurship at Depok 2 Vocational High School was using (a) role modelling,(b) Integrating learning inside and outside the classroom, (c) school culture assimilization, (d) strengtening. This school needed to develop the learning model for entrepreneurship education which was synergic between character education and entrepreneurship learning. The integration was implmented by accomodating intervention: cultur:structure and figure. The implementation covered: (1) classroom learning, (2) co curricul extracurricular, (3) learning through school culture, and (4) learning activities at home and in the community. Keywords: learning model, entrepreneurial character, vocational high school ABSTRAK
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".