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Record W4411410213 · doi:10.3828/qs.2025.4

“Competitive on Many Levels”: Nationalism, Neoliberalism, and Misinformation in the 2023 Québec Tuition Debate

2025· article· en· W4411410213 on OpenAlexaffabout
Jacob Robbins-Kanter, Daniel Troup

Bibliographic record

VenueQuebec Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsBishop's University
Fundersnot available
KeywordsMisinformationNeoliberalism (international relations)NationalismGovernment (linguistics)SubsidyPoliticsPolitical scienceIgnorancePublic administrationOptimal distinctiveness theoryPolitical economySociologyLawPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.343
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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