THE STANCE OF PEDAGOGICAL IMPARTIALITY REQUIRED BY QUEBEC’S ETHICS AND RELIGIOUS CULTURE CURRICULUM: IS IT CONSISTENT WITH TEACHER AUTONOMY AND CHARTER RIGHTS?
Bibliographic record
Abstract
This paper draws attention to certain regulatory difficulties raised by the State’s requirement that teachers remain “impartial” while teaching Quebec’s Ethics and Religious Culture curriculum (ERC). After detailing the impartiality requirement and its rationale in the context of a mandatory regime of religious education like ERC, the paper then considers, in light of Canadian jurisprudence, the extent to which the impartiality requirement can be squared with teachers’ legally recognized rights to professional autonomy, religious freedom and freedom of speech. The jurisprudence does not provide decisive answers to these questions, the paper shows, but it does reveal that ERC’s pedagogical impartiality requirement is out of joint with these key aspects of the broader regulatory framework in which Quebecois and Canadian teachers work. The paper closes by suggesting that these regulatory tensions reveal an important vulnerability in Quebec’s ability to maintain ERC since its mandatory stance of pedagogical impartiality has proven to be crucial in the two constitutional challenges to the curriculum heard by the Supreme Court of Canada.
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 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".