Working towards more socially just futures: five areas for transdisciplinary literacies research
Bibliographic record
Abstract
Abstract Policy‐makers and provincial governments have a responsibility to prioritise equity, diversity, inclusion and accessibility (EDIA) with approaches that leverage both intersectionality and transdisciplinarity, especially when looking at literacies research. Supported by a federally funded knowledge synthesis grant that surveyed the scope of EDIA in Canadian schools, this article focuses on youth marginalisation to address literacies learning. The authors address five concepts from a three‐phase literature review to examine inclusive practices that respect, acknowledge and address EDIA in K‐12 education. Across reviewed studies, there is an underlying trajectory outlining methodological challenges in implementing EDIA practices. We advance anti‐racist and abolitionist approaches by addressing five areas: (1) making learning more accessible by adopting culturally responsive pedagogy informed by local cultures, languages and values; (2) pursuing sustainable professional development in culturally inclusive teaching practices; (3) creating safer school environments that nurture community‐driven relationships between parents, students and their teachers; (4) reforming educational policies to concretely address structural racism, discrimination and misrepresentation of socially marginalised students by disrupting what is conceptualised and accepted as ideal culturally responsive pedagogy; and (5) prioritising community perspectives and input curriculum decisions to support underrepresented students. Ultimately, this article echoes this issue's orientations as it explores transdisciplinary practices composing an evolving understanding of literacies.
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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 | 0.099 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.037 | 0.035 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".