Critical Conversations on Indigenization and Internationalization in the Era of Equity, Diversity, and Inclusion
Bibliographic record
Abstract
As scholars and practitioners in the higher education fields of internationalization (Beck) and Indigenous education (Pidgeon), and as colleagues in a Canadian Faculty of Education, we have observed how we are often set up in adversarial positions for institutional and government resources. Recognizing the colonial roots of the university system and the legacy of colonization at home and abroad, we asked ourselves if our efforts in seeking principled internationalization and Indigenization had more in common than we realized. This session presents a dialogue between us on the incommensurabilities, possibilities, potential, and futures in pursuing Indigenization and internationalization within institutional spaces that were never set up to support these processes. Reflecting the process that unfolded in our exploration, we begin our conversations by applying postcolonial, decolonial and Indigenous analyses to both projects. We then demonstrate how internationalization and Indigenization occupy “generative spaces of tension”, at the liminal interface of neoliberal and critical orientations as articulated by Andreotti, Stein, Pashby, and Nicolson (2016) in their social cartography on internationalization. We then discuss the implications for Indigenization and internationalization in the face of the latest institutional initiatives emerging in Canadian higher education institutions on Equity, Diversity, and Inclusion.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.083 | 0.080 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".