Museums as sites of belonging, empowerment, and multimodal literacies for immigrant and racialized families
Bibliographic record
Abstract
This paper explores how museums can serve as transformative spaces for early childhood literacy, identity formation, and belonging, particularly for racialized immigrant children. Moving beyond print-centric definitions of literacy, it positions museums as multimodal environments where young learners engage with language, sound, image, and movement to make meaning. Drawing on a personal vignette of visiting the Royal Ontario Museum with my children, I illustrate how these encounters become acts of literacy, cultural affirmation, and identity negotiation. For children navigating multiple languages and cultures, museums offer spaces of imaginative play and embodied learning that resist deficit narratives and assimilationist expectations. Through a social justice lens, this paper frames museums as pedagogical counterspaces that center the cultural and linguistic assets of marginalized communities. It highlights the power of everyday museum interactions to support intergenerational connection, affirm cultural identities, and foster agency. By recognizing racialized immigrant children as active meaning-makers and co-creators of knowledge, museums can evolve into relational spaces that reflect and respond to the diverse communities they serve. This work calls for a reimagining of early childhood literacy education, one that honors the lived experiences, cultural wealth, and epistemologies of racialized immigrant families.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".