Post-school transition planning: creating a pathway to success
Bibliographic record
Abstract
Supporting students with diverse abilities as they transition from the K-12 education system to post-school life requires appropriate transition planning and programming. However, from the author’s perspective this has not been a major focus in education in British Columbia, Canada. The literature review explores how the United States, and many other countries, have developed best practices and programming for post-school transition planning, as well as laws requiring it begin by age 16 for all students, especially students with diverse abilities. This study used a mixed methods approach to investigate the current knowledge levels and beliefs about postschool transition planning with the goal of influencing the degree to which it is included in classrooms across British Columbia. The hypothesis is that post-school transition planning was not a well-known topic in education in British Columbia, nor was it widely being implemented. An online questionnaire and follow-up semi-structured interviews were used to gather data from 29 individuals. Three individuals participated in both the questionnaire and interview, while the remaining 26 completed only the questionnaire. This study found that while more post-school transition planning is being done than initially believed, it is not being done in an explicit, consistent, or structured way across the province. Implications are shared for the education system, including an increase of province-wide over-sight and increased teacher training; and individual teachers, including making it a student-driven process that starts earlier in a student’s school career and directly teaching self-determination skills.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".