Do Support Mechanisms Ensure a Smooth Labor Market Transition? A Cross-Cultural Qualitative Study on Vocational High School Graduates in Turkiye
Bibliographic record
Abstract
Vocational education plays a vital role in promoting youth employment; however, vocational high school graduates in Turkiye face structural, social, and economic barriers in their labor market transitions. This study explores the cross-cultural challenges experienced by Turkish and Syrian vocational graduates, focusing on job entry barriers, employer perceptions, workplace experiences, and the role of policy incentives. Conducted as part of the Social and Economic Cohesion through Vocational and Technical Education Project (SEUP), in collaboration with the Ministry of National Education and funded by the EU Facility for Refugees in Turkiye (FRIT) with KfW, this research draws on semi-structured interviews with 154 stakeholders across five provinces. Findings reveal critical obstacles such as skills mismatch, job dissatisfaction, adaptation issues, and, notably for Syrian graduates, dependency on financial aid. The analysis is theoretically guided by Social Identity Theory (SIT) and draws practical insights from international vocational education models in Germany, the Netherlands, Canada, and Sweden. Policy recommendations include aligning curricula with industry needs, simplifying employment incentives, strengthening school-employer partnerships, and improving the public image of vocational careers through awareness campaigns and mentorship programs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".