Beyond the “Add and Stir” Approach: Indigenizing Comprehensive Exam Reading Lists in Canadian Political Science
Bibliographic record
Abstract
Abstract Have universities heeded the call from the Truth and Reconciliation Commission of Canada and taken concrete action to integrate and promote Indigenous scholarship in their classrooms? In the field of Canadian political science, this question is vital but underanalyzed. Indigenous knowledges, histories, languages, customs, legal traditions, systems of governance and research methodologies are integral to Canadian politics, but calls for indigenization have often not been met. By analyzing comprehensive exam reading lists for Canadian politics doctoral students in programs across the country, this article argues that a fractured approach to indigenization begins early on in the training of faculty. Indigenous content remains largely underrepresented on exam lists and siloed into Indigenous- or diversity-focused sections of the political science literature. Most Indigenous politics readings engage centrally with sovereignty and the Constitution, with very few exploring the political dimensions of residential schools, gendered violence and other contemporary political issues.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".