Bibliographic record
Abstract
Abstract This chapter critically analyzes the near silence from international relations scholars on the topics and perspectives of Indigenous peoples in relation to the study of global politics. In the broader context of this silence in the discipline, and the calls for post-secondary institutions to engage in processes of Indigenization or decolonization, this chapter focuses specifically on how IR is taught at the introductory level in Canada. It asks, Are IR teachers integrating Indigenous perspectives and content into the curriculum or following the stubborn trend of continuing to erase Indigenous people and perspectives? To answer the question, the authors have undertaken content analysis of 42 introductory IR course outlines from across Canada. Despite some signs of hope in the data, they reveal that the teaching of IR at the introductory level in Canada fails to adequately respond to these pressing calls to decolonize the curriculum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".