Cautionary Stories of University Indigenization: Institutional Dynamics, Accountability Struggles, and Resilient Settler Colonial Power
Bibliographic record
Abstract
Increasingly, a discourse of indigenizing is being articulated in United States higher education. This article contributes to the limited existing research that examines how indigenization processes, well underway in Canada, are able to transform post-secondary institutions and/or how transformation is resisted and contained. With attention to institutional dynamics, Native studies’ centering of community accountability, and patterns of settler-colonial power, the study centers the perspectives and experiences at one university of Indigenous students, faculty, staff, and community partners. Interviews reveal four tensions or challenges of indigenization. “Hidden contributions” are the result of Indigenous people bearing the burden of rectifying the institution’s default colonial practices. Many individuals attempt to satisfy a challenging “dual accountability” to both First Nations and the university. Contradictions and uneven advances across the university create starkly varying experiences and reveal both promising change and disappointment. Finally, participants envision going beyond indigenization and decolonization by centering Indigenous intellectual autonomy and increasing accountability to First Nations. Interpreting these experiences and perceptions through logics of inclusion, reconciliation, and decolonization, the study suggests strategic approaches to address these tensions in future efforts in Canada and the United States.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.059 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| 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".