MODEL PERENCANAAN SEKOLAH MENENGAH KEJURUAN PENYELENGGARA KELAS STANDAR INDUSTRI
Bibliographic record
Abstract
The purpose of this research is to formulate the model of the planning of the Organizer School of industry standard class. This type of research uses a qualitative approach with the location in SMK PIRI 1 Yogyakarta on the package of Light Vehicle Engineering expertise. Data collection techniques used interviews, observations, and documentary observations. Testing of data validity is done by triangulation of data, auditing, and review of an informant. The technique of analysis through data reduction phase, data presentation, conclusion. The results of the research indicate that the School Planning of industry standard class organizers involves partner industry in synchronizing the industry curriculum with the curriculum of the government into implementation curriculum, student selection, preparation of facilities and infrastructure, teacher competence and teaching materials. Industrial standard class planning is done through the selection phase of students in the third semester, the second phase of the industry-standard teaching and learning process in the third semester until semester VI, the third stage through competency test in the 6th semester.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".