Effective Transitional Plan from Secondary Education to Employment for Individuals with Learning Disabilities: A Case Study
Bibliographic record
Abstract
One of the major goals in the education and training of individuals with special needs is to prepare them forindependence. However, in the Malaysian context, parents who have special adolescents are in doubt as to whatwould be the future of their children soon after they have finished the vocational training. This case studyexplores the transitional needs and subsequently to develop an effective transitional plan from secondaryeducation to employment for Malaysian individuals with special needs. The sample comprises two high schoolspecial educators and four persons with learning disabilities who are at work. The findings were triangulatedamong five co-ordinators of Non-Governmental Organisation, as well as parents for the four persons withlearning disabilities and their employers. The findings reveal that transitional needs of individuals with specialneeds includes collaborative support system, job coaching, self-advocacy skills training, career guidance andtransition assessment, vocational training, trained transition personnel and transition services. The transitionprocess would be a collaborative process between the government and non-governmental sector. From thefindings, an effective transitional plan from secondary education to employment for students with learningdisabilities was drawn. Several implications have been drawn from this study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".