Intercultural Competence in Higher Education: A Normative Anchor, a Developmental Perspective, and a Discursive Approach
Bibliographic record
Abstract
How to advance intercultural competence among university educators has become an important topic in the discourse on internationalization of higher education. However, what constitutes intercultural competence, how to promote it, and toward what end remain to be questions that are subject to debate. This conceptual article aims to contribute to the debate by 1) proposing a normative anchor based on Habermas’ theory of “principle of universalization”; 2) presenting a developmental perspective on intercultural competence in the context of higher education; and 3) introducing a discursive approach to empirical research on intercultural competence. Drawing on examples from an empirical study, we demonstrate a major challenge facing university educators in teaching students from diverse cultural backgrounds and point out the gaps in the existing models of intercultural competence. We then explain first why Habermas’ theory of “principle of universalization” may be a guiding principle for defining and practicing intercultural competence; secondly how the discourse of intercultural competence can be enriched by incorporating perspectives from psychosocial developmental theories; and thirdly why a discursive approach to research is a fruitful direction for empirical studies of intercultural competence. Finally, we provide a discussion on the implications of our proposals for teaching and learning in higher education.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.098 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".