Towards Decolonising Teacher Education: Criticality, Relationality and Intercultural Understanding
Bibliographic record
Abstract
This paper critically examines two studies that investigated pre- service teacher learning in contrasting contexts: an international study visit and a local service-learning experience. We argue that it is useful to conceive of these experiences as intercultural, and propose that how ‘intercultural’ is theorised in the western academy is object-based with roots in colonialism. We contrast this with a relational logic described by Burbules [1997. A grammar of difference: some ways of rethinking difference and diversity as educational topics. The Australian educational researcher, 24 (1), 97–116] and Osberg [2008. The logic of emergence: an alternative conceptual space for theorising critical education. Journal of the Canadian association for curriculum studies, 6 (1), 133–161]. Using alternative framings we interrogate teacher–learner relationships, highlighting how they were adversely affected by hegemonic practices. Findings indicate that when an object-based, colonising logic is the dominant frame, pre-service teachers were more likely to use ‘Othering’ discourses. When relational, decolonising pedagogies were used, pre-service teachers were more able to begin to teach otherwise. We conclude by making a case for intercultural education to take on a critical relational stance that counters the hegemonic violences that continue to be caused by a colonial abyssal line.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.092 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".