Affordances of in-School Video Production for a Plurilingual Adolescent with Interrupted Formal Education and Refugee Experiences: A (Hopeful) Case Study
Bibliographic record
Abstract
As Canada is increasingly committing to refugee resettlement, there is a critical need to understand how youth from refugee backgrounds can be supported to achieve their full potential in Canadian schools. This multi-year qualitative ethnographic case study explores the affordances of in-school video production for one plurilingual adolescent learner with significantly interrupted formal education and refugee experiences. Youth from refugee backgrounds have been shown to utilize digital multimodal composing (DMC), defined as the use of digital tools to make meaning in multiple modes (e.g., languages, images, sounds, gestures), including video production, to express their identities and strengthen relationships. However, few studies have explored the affordances of DMC specifically for youth from refugee backgrounds with interrupted formal education, at school in their settlement contexts. Guided by sociocultural and multimodal approaches to literacy, as well as an identity investment perspective on participation in learning, the study identifies three thematic patterns of video-production affordances: (1) overcoming the language barrier; (2) showcasing fluency; and (3) countering deficit perceptions. The study helps educators and teacher-educators better understand the affordances of in-school video production for youth from refugee backgrounds with significantly interrupted formal education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".