Interculturality in Teacher Education in Times of Unprecedented Global Challenges
Bibliographic record
Abstract
As societies face unprecedented challenges that are global in scope and “more-than-wicked” in nature, educators and educational policy makers emphasize the importance of deepening knowledge about the causes of these problems, creating policies to address them more efficiently, and offering more compelling moral arguments that might persuade people to change their convictions, and ‐ as a consequence ‐ their behaviour. These concerns shape how policies on the study of interculturality are approached in contemporary teacher education in our contexts in Canada and the UK. Our research, however, positions these as problems that cannot be solved with improved information, enhanced cross-cultural skills, or moral claims, because they are rooted in modernity’s systems, which structure the possibilities for co-existence on the planet. We see these problems as ontological challenges of being that emerge from a modernist ontology rooted in colonial violences. Our approach therefore explores an orientation to intercultural education which enables student teachers to expand their understanding of cultural and ecological relationships beyond existing frameworks of modernist knowledge, politics, and economic systems. In this paper, we share some of our current learning about the affordances and limitations of dominant approaches to intercultural education, and then explore how the method of “social cartography” can enable engagement with ontological problems in teacher education in a way that generates possibilities for imagining decolonial learning futures, beyond modernity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.062 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".