Pathways from school to work: A sequence analysis of non‐engaged youth
Bibliographic record
Abstract
INTRODUCTION: Research on heterogeneous pathways in school-to-work transitions (SWT), particularly longitudinal research, has been limited, as have empirical studies examining effective interventions for facilitating multiple SWT pathways among non-engaged youth (NEY), who are generally at risk of being not in education, employment, or training (NEET). METHODS: To develop a typology of SWT pathways, we conducted sequence analysis with longitudinal data from a sample of 630 NEY aged 14-29 (M = 19.78; 63.65% males) in Hong Kong during a 22-month period beginning in September 2020. We also performed multinomial logistic regressions to assess the impact of career and life development (CLD) interventions on SWT outcomes. RESULTS: Our analysis yielded a fivefold typology of SWT pathways: the Employment/Entrepreneurship cluster (31.27%), the Vocational Education and Training cluster (13.49%), the Generic Education cluster (16.83%), the Serious Leisure Development cluster (15.24%), and the long-term NEET cluster (23.17%). NEY in the intervention group receiving CLD services, inspired by the expanded notion of work (ENOW) and youth development and intervention framework (YDIF), demonstrated significantly higher likelihoods of being in the Employment/Entrepreneurship (OR = 34.5, 95% CI [10.53, 105.08]), Generic Education (OR = 3.74, 95% CI [1.81, 7.74]), Vocational Education and Training (OR = 1.55, 95% CI [1.05, 6.26]), and Serious Leisure Development (OR = 1.77, 95% CI [1.04, 4.46]) clusters than the long-term NEET cluster. CONCLUSIONS: Our findings highlight the dynamic, heterogeneous nature of NEY's CLD journeys, including that CLD interventions based on ENOW-YDIF have had a beneficial effect on NEY's multiple SWT pathways.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".