Indigenous Course Requirements: A Liberal-Democratic Justification
Bibliographic record
Abstract
In Canada, several universities have recently implemented course requirements in Indigenous studies as a condition of graduation, while others are considering following suit. Policies making Indigenous course requirements (hereafter ICRs) compulsory have caused considerable controversy. According to proponents, a main purpose of ICRs is to address historical wrongs and to foster a more complete understanding of the ongoing relationship between Indigenous and non-Indigenous citizens. According to critics, making such courses compulsory effectively imposes illiberal restrictions on university students and faculty by limiting the epistemic aim of free inquiry, while wrongly prioritizing concern for the welfare of one social group over others. In this essay, we propose a liberal-democratic justification for ICRs that addresses these two worries about the ideals that may underwrite these courses. We argue that ICRs can be justified in liberal democratic terms insofar as they foster knowledge of what John Rawls refers to as ‘the constitutional essentials’ and remediate civic forms of what Miranda Fricker refers to as ‘epistemic injustices’. Universities, we claim have highly plausible role responsibilities to promote the civic epistemic aims identified by Rawls and Fricker, which are especially weighty due to the power university degrees confer, as part of the formation of a “democratic elite”. We then defend this line of argument against objections on the basis of academic freedom, by arguing that universities have reasons, internal to the search for truth to champion the political aims we identify.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".