Bibliographic record
Abstract
This paper explores some of the challenges associated with Indigenizing Canadian universities. Like Indigenous scholars elsewhere, we seek guidance on how to undertake university Indigenization, and failing to find other examples we have decided to share our experiences here. This case study describes one event (hosting a feast and round dance) which provoked institutional policy reforms. We identify the ways in which our struggle to reform policy was often hampered by epistemic ignorance (Kuokkanen, 2007). We also explan how we are coming to understand our responsiblities for also addressing epistemic ignorance at the same time as we are changing the organizations in which we work. oma masinahikanis kitâpahtamok ohi kâ-moniskâkocik kakwe-iyiniwastacik kihci-kiskinwahamâtowikamikohkwa. peyakwan oki iyiniwak kâ-atoskecik ekotowihk, kiskiyihtamok e-nohtepayihk awiyahk ta-nikânistahk. mistahi nanitonamohk tânisi ka-isi-nâkwaniyek mâka wiyawâw soskwâc âcimosowak oma e-isi-wâpahtâkik. Tâpiskoc oma peyak (e-kistipohk ekwa e-picicinihke) ekwa ki-tâwakiskamok ohi wiyasiwâcikanisa kakwe-miskotastâcik. ekota wâpahtamok poko kwayas kakwe takwastâcik wiyasiwâcikanisa ekosi nawâc ta-miyo-mâmawi-atoskewak.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.068 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".