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Record W4406937082 · doi:10.18357/ijcyfs154202422252

EDUCATIONAL PURSUITS AND GOALS FOR ADOLESCENTS WHO ARE BLACK AND IN CARE: INSIGHTS FROM YOUTH, CAREGIVERS, AND CHILD WELFARE STAFF IN ONTARIO, CANADA

2024· article· en· W4406937082 on OpenAlexafffundvenueabout
Christa Sato, Daniel Kikulwe

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsYork UniversityUniversity of Toronto
FundersYork University
KeywordsWelfareSociologyWelfare systemPsychologyGerontologyNursingPolitical scienceMedicine

Abstract

fetched live from OpenAlex

It is well established in the literature that Black children and youth-in-care are disproportionately overrepresented in Ontario’s child welfare system. Additionally, Black youth experience structural challenges with the education system. However, there is a lack of Canadian literature that explores the educational experiences of Black children while receiving child welfare services. This study examined the reports of youth, child welfare staff, and caregivers on the educational experiences of Black youth-in-care. In-depth interviews exploring the educational needs and goals of Black youth-in-care were conducted with 13 participants (3 child welfare staff, 6 caregivers, and 4 youth). Findings highlight that Black youth-in-care experience labelling, hypervisibility and invisibility, and harassment and bullying, all of which had an impact on how they navigated the school system, and contributed to internalized stereotypes and feelings of isolation and unbelonging. The findings also identify strategies for innovation and promoting the educational success of Black youth-in-care.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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 designObservational
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

Citations1
Published2024
Admission routes4
Has abstractyes

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