Indigenizing Work as “willful work”: Toward Indigenous Transgressive Leadership in Canadian Universities
Bibliographic record
Abstract
As Indigenous peoples employed at a university who are working to Indigenize it from within, in this article, we share our experiences, discuss some of our challenges, and show how we draw meaning and strength from Indigenous stories to ground us in our approach. We use Indigenous, anti-oppressive, anti-racist and decolonizing theories, Indigenous standpoints, embodied experiences, and emotive responses to make explicit the lived work realities of Indigenous people in mainstream universities. Through a dialogic approach, we trace one pathway for explicating Indigenous transgressive leadership in Canadian universities. In our discussion, we situate Indigenizing work as “willful work” (Ahmed, 2014). We call for a “strategic willfulness” as a constructive orientation, for Indigenous leaders to embrace, as we continue to confront the colonial, hetero-patriarchal and whitestream nature of Canadian universities. Most importantly, we underscore the need for Indigenous leaders involved in Indigenizing work in the university to draw from Indigenous epistemological and relational ethics in their leadership work, and to be strategically willful, interruptive and transgressive.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.074 | 0.065 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| 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".