Indigenizing Internationalization and Internationalizing Indigenization: Insights From a Virtual Study Abroad to Ireland, Jamaica, and Aotearoa/New Zealand
Bibliographic record
Abstract
This paper aligns with the themes found in “The Transitions of Online Learning and Teaching” and “Sustaining Positive Change,” and reports on the collaborative work of a faculty member and an instructional designer from the University of Saskatchewan, Canada, where Indigenization and internationalization are leading institutional priorities. Here we consider possibilities for greater collaboration between these disciplinary and programmatic imperatives for mutual benefit, which the shift to virtual learning during the Covid pandemic enabled. We explore the capacity of Virtual Study Abroad course design to synthesize Indigenous and Western pedagogies and methodologies to conceive of innovative curriculum consistent with the negotiation of epistemological third spaces through the design of a Virtual Study Abroad course focusing on educational systems in Ireland, Jamaica, and New Zealand. Major themes emerging from the data include the capacity of virtual learning to enhance the democratization of knowledge and the potential of participatory pedagogies and innovative assessment approaches to decolonize postsecondary curriculum. Ultimately, we hope that this work will serve to inform new institutional models and approaches, whereby Indigenization strategies serve to decolonize internationalization programs, and Indigenization efforts benefit from innovative programming emanating from internationalization initiatives. Such a reconceptualization holds the promise of mobilizing Higher Education in the service of social justice and the ‘global good.’
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".