“Competitive on Many Levels”: Nationalism, Neoliberalism, and Misinformation in the 2023 Québec Tuition Debate
Bibliographic record
Abstract
How did misinformation about tuition levels throughout Canada emerge during Québec’s 2023 tuition reform? In October 2023, the Québec government announced plans to increase undergraduate university tuition fees for out-of-province Canadian students from $8,992 to approximately $17,000 per year. The stated purpose of the reforms was to end government subsidies of approximately 100 million per year to out-of-province students, redirect funds towards French-language universities, and reinforce the presence of French in Montréal. In defending the reforms, the Québec government claimed that, even after the increase, out-of-province tuition fees would be comparable to those in other provinces. This was incorrect, as remarkably few undergraduate students in the rest of Canada pay tuition fees approaching $17,000 per year, either in their home province or in other provinces. This article argues that government representatives’ false claims resulted from an effort to reconcile tenets of nationalism, neoliberalism, and educational accessibility. The policy was part of a nationalist governing agenda, which is articulated through neoliberal governing reflexes, but educational accessibility is an integral part of Québécois nation building and national distinctiveness. Ultimately, this case of misinformation represented more than strategic dishonesty or straightforward ignorance. It stemmed from an attempt to rhetorically preserve deeply embedded political values.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.043 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".