Unraveling the school-to-work transition of non-engaged youth: A continuous-time Markov chain analysis
Bibliographic record
Abstract
It is widely acknowledged that school-to-work transitions (SWTs) for non-engaged youth (NEY) often involve not just one status change but multiple status changes, and are frequently dynamic, nonlinear, and complex. However, less attention has been paid to analyzing and visualizing the transition from one status to another, especially under different interventions, within the SWT process. In our study, we utilized longitudinal data from a sample of 228 NEY aged 14–29 ( M = 18.90; 48.25 % males) in Hong Kong to examine the changes in transitional status among NEY using Markov chain analysis. Additionally, we compared the changes in the transitional statuses between NEY who received only information-oriented interventions and those who received both information-oriented and experiential interventions. Our analysis presents a transition probability matrix that quantifies transition probabilities among not only conventional education and employment statuses but also NEY’s new pursuits, such as fill-in jobs, serious leisure development (SLD), and vocational education and training (VET). Additionally, our study highlights the significance of the fill-in job, VET, and SLD statuses, which showcase their multiple, complex, and critical functions deserving further attention. Furthermore, our research found a synergistic effect of combining the information-oriented and experiential interventions that bring about positive outcomes in terms of multiple status changes. In sum, our findings offer a nuanced yet comprehensive perspective for understanding NEY’s dynamic and complex transitions among different SWT statuses, shedding light on the varying multiple status changes under distinct interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".