Visioning Refugee-Background Youth’s Futures Through Digital Multimodal Composing: Teachers’ Tensions
Bibliographic record
Abstract
Digital multimodal composing (DMC), the use of digital tools to make meaning with multiple modes (e.g., languages, visuals, gestures) has been shown to help showcase and value refugee-background youth’s investment in school learning, including their visions for their future. However, studies involving DMC have focused less on examining teachers’ tensions around how they might help these youth actualize these visions and ensure their investment is acted on to effect real change in participation and opportunity, especially given some youth’s significantly interrupted formal education. This qualitative case study employs the theoretical construct of investment to examine the tensions shared by four Canadian teachers regarding support for their refugee-background learners in actualizing their visions for the future, visions made visible through DMC projects. The study includes implications for educators and teacher educators who strive to address the needs of these learners.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".