Inquiry for Social Transformation: Black Mother Scholars Redefining Scholarly Inquiry Through Black Artistic Expression
Bibliographic record
Abstract
Writing, dancing, drawing, spittin’ rhymes, and other artistic expressions have long offered us, Black mother scholars, opportunities to reaffirm our humanity amid oppression (Fearon, 2023). Art offers Black mother scholars space to reconceptualise inquiry in ways that engage our families, challenge injustices, and usher social change within the educational milieu and beyond. The centring of Black artistic expression in educational research invites Black mother scholars to affirm the parts of ourselves, our families, and our communities that dominant forms of inquiry and anti-Blackness have sought to discredit. Educational research grounded in Black artistic expression compels us all to reimagine scholarly inquiry for social transformation. In this paper, I critically reflect on an arts-informed research study I led with a group of Black Canadian mothers who are adult literacy learners. In this reflexive piece, I explore how grounding research practices in Black art allows opportunities for storytelling, story listening, and Black refusal. Specifically, this paper explores the ways Black art supports researchers in addressing power differentials inherent in inquiry processes. The paper concludes with a series of reflective questions challenging scholars, especially Black mother scholars, to redefine traditional academic boundaries and recommit to social transformation through the arts.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.040 | 0.097 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".