Intercultural Dialogue on Indigenous Perspectives: A Digital Learning Experience
Bibliographic record
Abstract
This research explores how intercultural dialogue through a Collaborative Online International Learning (COIL) project enhances students’ understanding and integration of Indigenous perspectives. The initiative connected Norwegian Early Childhood Teacher Education (ECTE) students with Canadian Teacher Education students to explore Sámi and Métis cultures. Using a qualitative design, focus group interviews with ECTE students employed a hermeneutic approach to interpret experiences and cultural reflections. These insights, analyzed systematically, demonstrated the COIL project’s effectiveness in facilitating intercultural dialogue, fostering intercultural competence, and encouraging self-reflection among participants. Participants developed invaluable skills for integrating Indigenous perspectives into future educational roles, supported by facilitation that enhanced cross-cultural dialogue and language skills. This study underscores the need for frameworks supporting sustained cultural engagement, acknowledging sample size limitations. Findings advocate for the broader integration of intercultural collaborations in strategies, emphasizing education that enhances cultural competence. Future research should expand with larger samples and varied cultures, using longitudinal studies to assess the impacts on professional development and optimize collaboration educational contexts.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| 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".