Learning in the workplace: Fostering resilience in disengaged youth
Bibliographic record
Abstract
OBJECTIVE: International reports on school-to-work transition make it clear that worldwide youth are at-risk for educational disengagement and are three times as likely to be unemployed as their adult counterparts. Work-based education (WBE) is one of the most frequently recommended solutions for youth disengagement which suggests that WBE serves as a protective factor and encourages resilience in at-risk youth. The objective of this study was to describe and compare the experiences of two at-risk youth enrolled in WBE. PARTICIPANTS: Two 18-year old at-risk youth enrolled in WBE were chosen for study because they were learning in workplaces judged likely to promote resilience. Both had been disengaged from school prior to enrolling in WBE. METHOD: Each multiple-perspective case study includes the perspective of the youth, the workplace employer, and the work-based educator. Data consisted of ethnographic observations and interviews conducted at the workplace, and with the teacher in the school. RESULTS: Each case study highlights how supportive adults and an at-risk youth engage in interactions that facilitate the emergence of resilience in the workplace. CONCLUSIONS: In these two cases, risk and resilience are context specific, suggesting that at-risk youth may require tailored workplace programs to meet their career development needs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".