Toward a modular understanding of school-to-work transitions: Comparing Italy and Austria
Bibliographic record
Abstract
Comparative analyses investigating school-to-work transitions (SWT) aim to explain how institutional characteristics shape national differences in the transition from education to employment. Researchers often rely on typologies and classifications to simplify the complex processes involved. While typologies serve as useful heuristic tools, they can also lead to oversimplification and neglect of the multilevel governance structures and territorial disparities. Our modular approach integrates various research strands to enhance understanding of the relational and spatial dynamic underlying the transition from education to the labor market. We use analytical dimensions from previous studies to structure a small-N comparison, accounting for a higher degree of complexity. Empirically, we explore the theoretical argument through the in-depth comparison of SWT systems in two diverse cases with contrasting outcomes: Italy and Austria. The analysis reveals significant hybrid traits in both countries that are often overlooked by SWT typologies. In addition, we gain insights into how multilevel institutional configurations interact with the socio-economic context contributing to diverging SWT outcomes.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".