Bibliographic record
Abstract
Breaks the deafening silence of Indigenous women’s voices in academic leadership positions. Since the 2015 release of the report on the Truth and Reconciliation Commission of Canada, new Indigenous policies have been enacted in universities and a variety of interconnecting Indigenous senior administrative roles have been created. Many of these newly created roles have been filled by Indigenous women. But what does it mean for Indigenous women to be recruited to Indigenize Western institutions that have not undergone introspective, structural change? Informed by her own experiences and the stories of other Indigenous women working in senior administrative roles in Canadian universities, Candace Brunette-Debassige explores the triple-binding position Indigenous women often find themselves trapped in when trying to implement reconciliation in institutions that remain colonial, Eurocentric, and male-dominated. The author considers too the gendered, emotional labour Indigenous women are tasked with when universities rush to Indigenize without the necessary preparatory work of decolonization. Drawing on an Indigenous feminist decolonial theoretical lens and positioning Indigenous story as theory, Brunette-Debassige illustrates how Indigenous women can and do preserve and enact their agency through resistance, and help lead deeper transformative changes in Canadian universities. Ultimately, her work provides a model for how reconciliation and Indigenization can be done at an institutional level.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".