Tertiary dance education in inclusive settings: teachers’ intercultural sensitivity for teaching international students
Bibliographic record
Abstract
In internationalised dance education, many teachers undertake inclusive philosophies and identify themselves as inclusive educators. But there may be possible differences between theories in minds and practices in studios, leading to the research inquiry: How might dance teachers’ teaching practices for international students extend from their intercultural sensitivities in inclusive-oriented tertiary education? In this research, international students are identified as individuals who move across national boundaries from Eastern countries to enrol in Western institutions. Participants are recognised as self-defined inclusive educators who have teaching experiences with international students. The research utilises Milton Bennett’s Developmental Model of Intercultural Sensitivity as a critical framework to understand different intercultural competencies. The framework also guides the demonstration of results in a sequence: Denial, Defense, Minimisation, Acceptance, Adaptation and Integration. The results are tangible narratives that indicate different intercultural sensitivities from participants with inclusive teaching intentions. Data were gathered through semi-structured interviews and analysed in a qualitative content analysis process. The study implies the gap between teachers’ inclusive intentions and teaching experiences, which could facilitate a reflection on educational inclusion for international dance students.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| 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".