(De)Colonized Science: Hopes, Complexities, Tensions, and Frustrations in Seeking to Indigenize Undergraduate Science Education
Bibliographic record
Abstract
This article is an exploration of our efforts to develop an Indigenous Science Course at Mount Royal University (MRU) located in Mohkinstsis within the Ancestral Lands of the Blackfoot Confederacy the Territory of the Treaty 7 signatories Kainai, Piikani, Siksika, Tsuut’ina, Bearspaw, Chiniki, and Wesley Nations and the Metis Nation Region III. The authors are an Indigenous environmental scientist and recent MRU graduate (Nikita), a settler assistant professor (Collette), and an Indigenous assistant professor (Joshua). We engage here as an enactment of research as ceremony (Wilson, 2008). We draw on Metissage storywork to spark meaning making of our experiences in seeking to contribute to the Indigenization of our University (Archibald, 2008). We believe that the stories we share have the potential to open up interpretive possibilities for those interested in Scholarship of Teaching and Learning as Reconciliation (Hill, 2022) and decolonization and Indigenization of post secondary education more broadly (Battiste, 2013). Through storytelling we endeavor to push for change in sharing the hopes, complexities, tensions, and frustrations we encountered.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.036 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".