Attitudes envers le langage inclusif au Québec en 2022 : associations avec le genre, l’âge et le contact universitaire selon une analyse multivariée des données d’un sondage en ligne
Bibliographic record
Abstract
Introduction: The province of Quebec (Canada) is often considered to be at the vanguard of language feminization in the francophone world (Elchacar, 2019). However, even in Quebec, there is no consensus on gender-inclusive language, with public opinions and even official approaches varying considerably (Dumais, 2007; Elchacar, 2019; Moreau, 2023; Rioux, 2020; Niosi, 2017). Objectives: This study aims to provide an up-to-date picture of attitudes towards inclusive language in the French-speaking population of Quebec, particularly how these attitudes relate to sociodemographic characteristics. Methods: An online, 15-item Likert-scale survey conducted in Spring 2022 gathered complete responses from 193 French-speaking adult Quebecers (M=36; F=146; Other=11) regarding their attitudes towards gender-inclusive language, while also recording the following sociodemographic characteristics: (1) age (18-30; 31-50; 51;-65; > 65), (2) gender (male, female, other), and (3) university affiliation (having worked or studied at a university previously). An overall attitude score was computed for each participant by averaging their responses to the survey items. Results: Results of a PERMANOVA show that while attitudes towards inclusive language are generally positive (especially amongst younger and non-male-identifying participants), significant disagreements persist, notably along sociodemographic lines, with age and gender accounting for approximately 20% of the variance in the overall attitude score (R² = 0.195). University affiliation was not a significant factor (p > 0.05). Conclusion: Overall, the study confirms that attitudes towards gender-inclusive language in Quebec remain heterogeneous and are associated with age and gender, though only partially accounted for by them. This study provides a foundation for future research using larger, more representative samples and will facilitate cross-regional comparisons of attitudes towards gender-inclusive language in the francophone world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".