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Record W4361002174 · doi:10.18357/ijcyfs141202321287

THE MANY FACES OF THE “FOSTER CARE YOUTH” LABEL: HOW YOUNG WOMEN MANAGE THE STIGMA OF OUT-OF-HOME PLACEMENT

2023· article· en· W4361002174 on OpenAlexaffvenue
Mathilde Turcotte, Nadine Lanctôt

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversité de SherbrookeCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsFoster careStigma (botany)PerceptionNarrativePsychologyFocus groupFoster parentsDevelopmental psychologyClinical psychologyNursingMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.303
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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