Beyond Folklorama: a mixed-methods phenomenological study on the intercultural competence of preservice teachers
Bibliographic record
Abstract
While Manitoba has made significant inroads in educational reform, the overall design and practice of education in the Province is still largely skewed toward a Eurocentric ideal. One of the factors contributing to this situation is the unpreparedness of prospective teachers to fully recognize and embrace the diversity present in their classrooms. This study used a mixed-methods approach, comprising surveys and interviews, to determine the characteristics that underlie the intercultural competence of a subset of preservice teachers. Ten (n=10) preservice teachers, recruited from a mandatory cross-cultural education course, were surveyed and/or interviewed to determine their thoughts, behaviours, and attitudes towards people from different cultures. Data collected during the first phase was assessed to uncover features of their intercultural sensitivity. Information gathered from follow-up face-to-face phenomenological interviews illuminated details of their lived-experience of intercultural competence. Findings revealed that the preservice teachers share ten characteristics of intercultural competence.Data gleaned from this investigation may be used by preservice, novice, and experienced teachers, as well as faculty and administrators of teacher education to reform the design and practice of multicultural education in grade school and 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".