THE MANY FACES OF THE “FOSTER CARE YOUTH” LABEL: HOW YOUNG WOMEN MANAGE THE STIGMA OF OUT-OF-HOME PLACEMENT
Bibliographic record
Abstract
A number of studies have found that adolescents in foster care expect and perceive stigma related to their “foster care youth” status. Yet, little is known about how this perceived stigma manifests, as well as how youth manage it. The current study therefore aimed to explore how young women with a history in foster care integrate these experiences into their life stories. The focus is on discursive manifestations of stigma in participants’ narratives about placement in foster care, their own perceptions of care-experienced girls and women, as well as how they self-present. Special attention is also given to the ways in which youth try to reduce, deflect, or eliminate stigma. The present study draws on semi-structured interviews conducted with a sample of 20 young women with a history in foster care. Our findings suggest that participants do anticipate and perceive public stigma in relation to their history in foster care. The results also highlight the various strategies used by participants to resist self-stigmatization. The main strategy used was to distance themselves from their “foster care youth” status, insisting that they should never have been placed in foster care and that they are not faring badly as adults, unlike typical care-experienced youth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".