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Record W3155287181 · doi:10.53967/cje/rce.v44i1.4979

Low-Income Black Parents Supporting Their Children’s Success through Mentoring Circles

2021· article· en· W3155287181 on OpenAlexaffvenueabout
Alana Butler

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsQueen's University
Fundersnot available
KeywordsDisengagement theoryCritical race theoryRacismGender studiesEthnic groupPsychologyLow incomeRace (biology)SociologyGerontologySocioeconomics

Abstract

fetched live from OpenAlex

This article presents the results of a parent engagement project called “Mentoring Circles.” The project focused on the needs of low-income Black parents who have children enrolled in the Toronto District School Board. Two focus groups, with seven to eight Black parents in each group, were conducted during the summer of 2018. The study drew on theories of community wealth and funds of knowledge (Gonzalez et al., 2005; Yosso, 2005), Black feminist theory (Collins, 2000; Crenshaw, 1991), and critical race theory (Delgado & Stefancic, 2012). The Black parent narratives served as counter-stories to stereotypes about Black parent disengagement in low-income communities. The low-income Black parents in the study were very engaged in their children’s education and were invested in their academic success. The Black parents strategized to support their children’s education by forming supportive peer mentoring networks and advocating for their children though relationship-building. The findings suggest that mentoring circles could serve as a model for engaging Black parents in the support of their children’s academic success. Keywords: Black Canadian children and youth, anti-Black racism, Black parents and students, low socio-economic status, race and ethnicity, social class

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)0.001
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 score0.560
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.313
Teacher spread0.273 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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