Somali-Canadian, Muslim, Female YouTubers & Teachers Make Videos as a Global Literacies Practice
Bibliographic record
Abstract
Digital technologies require literacy educators to move from a unique focus on print-based literacies towards the integration of New Literacies across the K--–12 curriculum. Technologies such as digital video enable student collaboration, inquiry, communication, and sharing in-between classroom, community, and global contexts. The ability to negotiate difference on screens and face-to-face thus becomes ever more compelling. This chapter draws on theories related to global literacies, New Literacies, critical perspectives, and youth literacies, which are applied to video production, teacher education, and in-service professional development. To theorize video production as a global literacies practice, this chapter discusses examples of the video making experiences of: (a) racialized Somali-Canadian, Muslim, female YouTubers; (b) teacher candidates in an integrated elementary language arts/arts course; and (c) practicing educators at a digital literacies institute. The chapter provokes thought around creating educational spaces to promote more equitable social relations, and further expands what counts as literacy in our networked world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".