<i>I See You, Mama</i>: Low-Income Black Mother Leaders Reimagining Schools as Homeplace for Their Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".