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Record W4234759117 · doi:10.32920/ryerson.14654499

Former Youth in Care: Kinship Care and Its Potential Impact on Black Families & Cultural Identity in the Child Welfare System

2021· preprint· en· W4234759117 on OpenAlexaffabout
Frank Osei

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsKinship careWelfareKinshipWelfare systemFoster careCommissionPopulationPolitical scienceChild careIdentity (music)Economic growthSociologyMedicineEconomicsDemographyNursingAnthropology

Abstract

fetched live from OpenAlex

In 2016, the Ontario Human Rights Commission (OHRC) launched a public inquiry to determine whether or not there was a disproportionate number of racialized populations representing the child welfare system. Data collected from the Children’s Aid Society of Toronto (2015) showed that while African Canadians make up 8.5% of the Torontonian population, they made up 40.8% of the children and youth in the child welfare system. This alarming information called for changes in the ways Black children and youth have been impacted and what changes could be made with policy. This research study intends to highlight policies that have been implemented in response to over-represented communities in the child welfare system with a particular focus on kinship care and how it is incorporated into policy that seeks to improve the treatment and service for Black families in the Greater Toronto Area.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.564
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.008
Scholarly communication0.0060.003
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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