Remixing images, words, and ideas: Young emergent bilinguals composing remixed countertexts as a creative, relational, and decolonizing practice
Bibliographic record
Abstract
Sharing multimodal creations of four young racialized multilingual learners from a year-long education design research project, this paper argues that remixing and repurposing are decolonizing practices through which marginalized children create countertexts. Anh, Jordan, Sarah, and Kimi – children categorized as English language learners (ELLs) in a Grade 2/3 Western Canadian classroom – expertly designed countertexts using popular and digital culture, remixed drawings, and storied responses to mentor texts and classroom activities. Framed by perspectives of multiliteracies and culturally sustaining pedagogies, these remixed countertexts highlight non-dominant perspectives and bring young children’s diverse knowledges into the official space of the classroom.Thematic and visual analysis of the children’s oral, written, and visual countertext data highlights languages and literacies as relational practices; remixing as a creative composing process for emergent bilinguals; and remixed countertexts’ potential for supporting decolonial and antiracist reimaginations of emergent bilinguals’ participation and achievement. While individual and original productions are often most valued within Western educational systems, the creative processes that accompany borrowing, copying, and remixing in countertexts can transform language, texts, and practices as they are flexibly re-purposed and re-sourced. A critical appreciation of remixed countertexts encourages educators to resist normative and narrow conceptions of literacy, consider White middle-class subtexts, challenge colonial ideas of composition, and design collaborative, decolonizing, and antiracist pedagogies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".