“Activizing” the pedagogy of multiliteracies: The dynamic, action-oriented turn with languacultural landscape studies
Bibliographic record
Abstract
In this article, I introduce the approach that I have named languacultural landscape (LCL), which is the advancement of linguistic landscape (LL) used as a pedagogical resource. I draw on the pedagogy of multiliteracies (PoM) and explore the potential of an LCL project to bring PoM in its fullest “critical” sense to plurilingual classrooms. The paper discusses the theoretical foundations of the LCL approach and outlines the differences and similarities between LCL and LL as pedagogical resources. I also provide recommendations on how an LCL project could be conducted in a classroom, based on an LCL research project undertaken by me in my local community. I argue that such a project could be used to not only address students’ understanding of cultural diversity by critically analyzing historical and political contexts of learning, but also as a way to reimagine the reality around them with more egalitarian cultural dynamics in mind.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".