Toward a Culturally Inclusive Canon of Multimodal Picture Books: Developing Multiliteracies Practices and Assessments for Ontario’s Classrooms
Bibliographic record
Abstract
Multimodal picture books are a critical component of children’s literacy development, and in a multicultural province such as Ontario, it is vital that literacy development include cultural literacy. The demographics of the province’s classrooms are increasingly diverse; however, minoritized cultures are underrepresented among teachers, and there are sparse training mandates related to cultural inclusion. Thus, Ontario’s culturally diverse student body is encountering a number of barriers related to gender, ethnicity, perceived race, sexual identity, ability, class, and other social markers. To provide teachers with the tools needed to support their students, the current study utilizes a theoretical framework derived from anti-oppressive practices to identify where students encounter barriers. The work examines the strengths and limitations of the traditional canon of children’s multimodal picture books and explores the ways in which more inclusive works can support a culturally inclusive learning environment. Based on this, a culturally responsive selection process is outlined. The study employs a multiliteracies framework to propose classroom activities and assessment models that promote and assess literacy development. Transformative teaching approaches are also recommended to help teachers broaden their understanding of culture. Additionally, recommendations are made regarding mandated cultural training for pre-service and in-service teachers, as well as curriculum reform.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".