Experiences and Trajectories of Former Youth in Care
Bibliographic record
Abstract
This dissertation draws on semi-structured interviews with 20 former youth in care to examine their experiences in Ontario’s child welfare system and the long-term impacts of those experiences. Using a symbolic interactionist approach, the study analyzes the biographical disruption that experiences in the care system represented for participants and how this affected their life trajectories. The findings are organized and discussed around three themes: a) participants’ involvement with the system – how they experienced entering, being in, and exiting the system; b) the stigma participants experienced while in care, and their efforts to neutralize or manage the stigma; and c) the impact that their care experiences had on participants as adults. The data reveal a range of challenges that participants encountered while they were in care, including loneliness, isolation, neglect, general mistreatment and in some cases, abuse. Particularly damaging were the stigma and assaults on “self” that participants experienced as a result of their care status. The data also reveal that in one way or another, these early experiences followed participants into their adult lives, leaving them with a myriad of issues and concerns. The dissertation ends with a discussion of the substantive and theoretical contributions of the findings, as well as a section that addresses the policy implications of the research.
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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 | 0.003 | 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 | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".