Drawing on Cope and Kalantzis' Transpositional Grammar to Explore L2 Identities through Multiliteracies
Bibliographic record
Abstract
This research uses a social justice lens to explore the ways in which L2 learners are better able to succeed in learning a new language when they feel emotionally and socially supported through literacy practices shaped by the principles articulated within a pedagogy of multiliteracies. In this study federally funded by a Social Sciences and Humanities Research Council (SSHRC) Insight grant in Canada, high schools, language learning councils, museums, art galleries, and other community-based organizations were included as sites to expand thinking about how multiliteracies and multimodality could be incorporated to significantly increase L2 learners&s; abilities to engage in additional language learning. The participants included teachers, students, adult educators, adult learners as well as policy makers and administrators. The research options that participants chose from were in-person interviews, observations, document analysis, and original film footage of classrooms and learning spaces. The themes discussed in this chapter are (1) The Transpositional Grammar of Agency; (2) Participation invites Social Justice Action; and (3) Learning by Design. This chapter theorizes data from this research study by drawing upon elements within Cope and Kalantzis&s; most recent work in terms of how they conceptualize agency, participation, and design to contribute to a transpositional grammar of meaning-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| 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".