Harmonizing Two of History Teaching’s Main Social Functions: Franco-Québécois History Teachers and Their Predispositions to Catering to Narrative Diversity
Bibliographic record
Abstract
This article presents the Quebec ministry of education’s (MELS) strategy for diversifying the national historical narrative that is transmitted in the province’s History and Citizenship Education program as well as the manner in which Francophone national history teachers put this strategy into practice. In bringing research on their social representations and historical consciousness together, this paper looks at some of the main challenges that these teachers face when specifically harmonizing two of history teaching’s central social functions for catering to narrative diversity. When seeking to adequately balance the transmission of a national identity reference framework with the development of autonomous critical thinking skills, it becomes clear that these teachers’ general quest for positivist-type, true and objective visions of the past as well as their overall attachment to the main markers of their group’s collective memory for knowing and acting Québécois impede them from fully embracing the diversification of the province’s historical narrative. The article ends by raising some important questions regarding the relevance of assisting teachers to authentically develop their own voice and vision for harmonizing the two aforementioned functions of history teaching and for being answerable to the decisions they make when articulating and acting upon such beliefs in class.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".