Otten / Intercultural Learning and Diversity Intercultural Learning and Diversity in Higher Education
Bibliographic record
Abstract
International learning experiences seem to shift from an added-value side effect to an all-persuasive motive in a market-driven and globalised educational sector. How-ever, nature and substance of this experience, target groups, and also means for unfold-ing this value are still vague when it comes to the time beyond the mission statements of internationalisation. This article presents the theoretical and conceptual frame-work of an understanding of intercultural learning. The first part will outline some assumptions about intercultural encounters and its meaning for intercultural learn-ing. The second part describes approaches of diversity activities with an institution-wide focus. Drawn from regions with an explicit diversity policy tradition in higher education—namely, the United States, Canada, and Australia—ways and problems of its adaptation to the European context will be discussed. The article provides an ori-entation for setting up diversity activities and diversity plans aimed at intercultural learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".