The educational experience of young people in residential care through the lens of learning careers
Bibliographic record
Abstract
Abstract To complement previous research investigating the educational attainment of young people in out‐of‐home care reporting lower educational outcomes and risk factors, a more comprehensive approach is necessary. Our objective with this article is to better understand the learning careers of young people in residential care. That includes documenting their experience and the meaning they attribute to learning in formal settings over time and identifying critical turning points in each young person's learning career. Based on an interpretative paradigm, we conducted a qualitative study using biographical interviews with young people aged 14–18 in residential care in Quebec, Canada. We first demonstrate that the learning careers of young people in residential care are unique and non‐linear. Second, by studying turning points, we highlight the critical role that institutions, teachers and youth workers can play in fostering a positive or negative learning career. Finally, we noted learners’ different responses to these circumstances and differences in learners’ perception of their measured performance and their influence on their engagement with formal learning. We discuss why considering young people as the main actors and producers of meaning regarding their educational experience and learning careers appears necessary.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".