"I have no story to tell": exploring the multimodal literacy identities of adolescents
Bibliographic record
Abstract
Adolescent students’ participatory and multimodal experiences outside of school are often at odds with entrenched in-school literacy instruction that tends to privilege print-based over other forms of communication. This research study documents the multimodal literacy identities of students in a Grade 8 classroom as they explore storytelling through different modalities. Drawing upon pedagogical documentation and critical discourse analysis research methodologies, five specific inquiry questions are investigated: 1) How do students already engage in multimodal literacy practices and analysis outside of school? 2) What strategies do students have for making sense of multimodal texts and how do they identify and discuss different modes of communication? 3) What assessment evidence can be gathered to show how and if students transfer and enhance meaning across ensembles of modes, and how can this assessment evidence be gathered in contextually relevant and respectful ways? 4) Is there evidence to suggest that students demonstrate an increase in deep or critical thinking about multimodal analysis after the instructional-learning cycle? 5) If students are allowed to voice their own stories in different modalities, what impact will this have on the classroom community and their personal literacy practices and identities? Results of this qualitative study contribute to closing the established gap between adolescents’ in-school and outside of school literacy practices, while offering pedagogical support to Manitoba educators as a new provincial ELA curriculum is introduced that advocates a multimodal literacy education environment.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".