Vocational education and training attrition and the school‐to‐work transition
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the issue of dual vocational education and training (VET) attritions as indicating difficulties in the transition from school to work. Design/methodology/approach The methodology consists of a content analysis of semi‐structured interviews with 46 young people who interrupted their dual VET during the first year. Findings The findings showed that VET “dropouts” experience transitional problems. These can be one of two sorts: diachronic or synchronic. Diachronic problems are related to difficulties with the shift from a standard school system to VET. Synchronic problems are due to difficulties in learning, relational or working environments. Research limitations/implications The results stress the need to widen the definition of transition and to consider the context in which the transition takes place. Further research could compare these results with employers' and trainers' points‐of‐view. Practical implications Accordingly, interventions should be taken before and after the precise moment of the shift from school to VET and should include all stakeholders of VET. Originality/value The paper encompasses three original aspects: it considers school‐to‐work transition as a process beginning before and ending after the concrete shift to VET, suggesting that a transition is achieved only when the person reaches a relatively stable situation on the workplace; consequently, it conceives VET attrition as an indicator of a failure of the school‐to‐work transition process; and it stresses the influence of the social and the learning environment on the quality of VET.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".