“Can I speak that यह प (here)?”: Emergent bilinguals navigating intersectional inequities with relational multimodal multilingual practices in elementary school
Bibliographic record
Abstract
This paper highlights the stories of two grade 2/3 emergent bilinguals, Jay and Pari, to underscore the need for more inclusive listening for the experiences of marginalized learners and to offer relational, multimodal, and multilingual pedagogical designs. Framed by theories of intersectionality, translanguaging, and multimodality, I present moments from a collaborative multilingual photo project, Something/Someone I love, and center Jay and Pari’s identities – newcomers marginalized as ELLs, racialized students from low-income families – and their gendered experiences. The children negotiated the identity categories within the classroom’s social and academic worlds and shifted their deficit identities. Jay took up writing in Spanish and digitally editing a favorite car photo; Pari shared a photo of her stuffy, reflecting multiliterate competencies in her Hindi and English narratives. Jay and Pari drew on their communicative repertoires for connection and achievement, transitioning their languages, experiences, and emotions as valued resources. Yet this shifting of identities was not straightforward and normative English-only discourses were apparent. Three themes connect their stories: bridging multimodal and multilingual practices to celebrate capacities; focusing on relational and emotional processes to invite children’s experiences; and challenging dominant language ideologies for equitable learning. Implications center on redesigning the relationship between children’s lived experiences and school practices toward more inclusive listening for equity and relationality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".