Implementation of Specialized Vocational Subjects for Students with Learning Disabilities in Career Transition: A Case Study
Bibliographic record
Abstract
Purpose -This study explores the implementation of Special Vocational Subjects (MPV Khas) for students with learning disabilities (MBP) as career transition from teachers' perspectives in the Special Education Integration Program (PPKI).The study employs Maslow's Theory and Behaviorism, using the Transition Programming Taxonomy Model, Transition to Adult Living Model, and the Generic Skills Model from The Conference Board of Canada.A qualitative approach with a multiple case study design was used, involving eight teachers selected through purposive sampling from four secondary schools.Data collection included interviews, observations, and document analysis, which were analyzed descriptively and presented narratively.The findings indicate that MPV Khas implementation in PPKI covers program structure, career experience preparation, generic skills application, parental involvement, and inter-agency collaboration.However, challenges exist in curriculum structure, lack of qualified teachers, financial constraints, student attitudes, and limited parental and agency involvement.A new framework is proposed to enhance the implementation of MPV Khas for career transition in PPKI.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".