At-risk Youth Find Work Hope in Work-Based Education
Bibliographic record
Abstract
The transition from school to the workplace has been identified as challenging for at-risk youth who have already disengaged from learning and feel disenfranchised in the context of school. Work-based education (WBE), including co-operative education, has been recognized in recent years as an effective strategy for enabling at-risk youth to re-engage with learning and to make more successful transitions to the workplace and to further education. Not all at-risk youth thrive in WBE, even in programs that are judged to be effective for most. What remains unclear is what changes for those previously disengaged youth, as a product of participation in WBE, that enables them to shift their perspective and re-engage with learning. The purpose of this paper is to describe the experiences and changes in perspectives, in their own words, of seven previously disengaged youth while they were participating in WBE. Their teachers recommended these youth because they had made a “turnaround” since beginning WBE. The experiences and changed perspectives reported by these seven youth suggest that they found work hope through their success in WBE, and were beginning to set goals, view themselves as agents, and seek pathways to reach their goals. We discuss implications for increasing the effectiveness of WBE to re-engage even greater numbers of at-risk youth and to facilitate their transition to work by enhancing work hope.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".