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Record W4408225630 · doi:10.3138/jcs-2023-0045

<i>I See You, Mama</i>: Low-Income Black Mother Leaders Reimagining Schools as Homeplace for Their Children

2024· article· en· W4408225630 on OpenAlexaffvenueabout
Stephanie Fearon

Bibliographic record

VenueJournal of Canadian Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsYork University
Fundersnot available
KeywordsLow incomeSociologyGender studiesMedia studiesSocioeconomics

Abstract

fetched live from OpenAlex

Canadian researchers continue to stress the importance of Black parent engagement in the schooling lives of Black children. Scholars, practitioners, and Black families call on schools to partner with Black parents and guardians to further support Black children’s academic achievement, well-being, and development. Although scholarship affirms the integral role of Black mothers, especially those with low income, in the educational lives of their children, limited research centres the work they lead at their children’s schools. In fact, low-income Black mothers’ work is often excluded from Canadian narratives on parent engagement and omitted from educational scholarship on leadership. Ultimately, low-income Black mothers and their contributions to their children’s schools are often rendered invisible in public and academic discourses. This arts-informed study took place at an Ontario school and traced the author’s work in establishing a family reading program with fourteen low-income Black mothers. The study drew on Black motherwork theory, Black feminist notions of homeplace, and Endarkened storywork to further understand Black mothers’ leadership at their children’s school. This article centres the narrative of one Black mother participating in the study describing how she leveraged collective work and technologies to eschew surveillance and establish a homeplace at her child’s elementary school.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.340
Teacher spread0.298 · 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 designNot applicable
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
Published2024
Admission routes3
Has abstractyes

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