Reconceptualizing literacy and disrupting Whiteness: Multiliteracies autobiographies in teacher education
Bibliographic record
Abstract
• Multiliteracies autobiographies offer generative spaces for teacher candidates to cultivate antiracism, practice critical reflexivity, and reimagine pedagogical possibilities for literacy education. • Through the three-part multiliteracies autobiographies, teacher candidates worked to denaturalize taken-for-granted notions of literacy, to de-silence race, and to centre linguistic diversity, equity, and antiracism in their pedagogical designs. • Critical engagement with identity, including through autobiographical and multimodal means, is a key component of disrupting Whiteness and English supremacy and developing antiracist praxis. • More must be done to cultivate brave spaces that support TCs to disrupt race-evasiveness and to productively grapple with systemic issues of race, language, and equity in literacy education. Dominant approaches to literacy education privilege White middle-class norms, creating urgent need to reconceptualize literacy and decentre Whiteness in teacher education. This study examines the role of three-part multiliteracies autobiographies in supporting teacher candidates (TCs) to reconceptualize literacy while considering possibilities for equity-oriented antiracist pedagogy. Conducted in a literacy methods course at a Canadian university, this critical action research study employs raciolinguistics, critical antiracism, and multiliteracies as theoretical lenses to investigate: How can multiliteracies autobiographies support TCs’ reconceptualization of literacy? How can these assignments contribute to TCs’ critical orientations towards antiracism, equity, and linguistic diversity? Analysis of multimodal autobiographies and critical reflections demonstrate TCs’ reimaginations of literacy in ways that denaturalize Whiteness, growing courage to critically engage with race, and development of concrete pedagogical ideas for more equitably supporting racialized multilinguals. Implications centre on heightening critical reflexivity and explicit engagement with race and identity in antiracist teacher education.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".