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Record W4416431959 · doi:10.1080/15210960.2025.2575741

Visioning Refugee-Background Youth’s Futures Through Digital Multimodal Composing: Teachers’ Tensions

2025· article· en· W4416431959 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMulticultural Perspectives · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFutures contractMultimodalityField (mathematics)Technology integrationQualitative researchKey (lock)Discourse analysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.304
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it