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Record W4239024286 · doi:10.32920/ryerson.14648682.v1

A hairy situation: child protection workers experiences providing hair care to African Canadian children in care

2021· preprint· en· W4239024286 on OpenAlexaffabout
Chantal Gray

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsAgency (philosophy)Child protectionWelfareNarrativeIdentity (music)RacismAfrican americanPublic relationsPolitical scienceSociologyPsychologyGender studiesSocial scienceLaw

Abstract

fetched live from OpenAlex

This research explores the experiences of Child Protection Workers regarding Black youth hair maintenance while in care, as hair is a significant identity marker for Black children. Additionally, it is important to understand the rights and responsibilities of the child protection workers, foster parents, biological parents and outside paid resources, as they all play different, but vital roles in a child’s life. The training that child welfare agencies provide and community resources will also be discussed. I began the study by providing an overview of the literature on the topics of Black hair and responsibilities of the child welfare agencies. A critical race theory framework will deepen the understanding of racism intertwined with hair; as well as exploring terms such as organization, whiteness and cultural competency. A narrative approach was used as the methodology and detailed within the findings section. The study found that there was inconsistency within the agency together with lack of resources to be used in the community.

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)0.004
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 score0.163
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0450.015
Scholarly communication0.0050.002
Open science0.0020.008
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.325
Teacher spread0.296 · 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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