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Divergent Histories: Narrative Asymmetry in French and English History Curricula in Canada

2025· article· en· W4417112443 on OpenAlexaboutno aff
Laurent Poliquin

Bibliographic record

VenueInternational Journal of Research and Innovation in Social Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumNarrativeFrenchIdentity (music)Australian CurriculumPoliticsNeuroscience of multilingualismTeacher education

Abstract

fetched live from OpenAlex

What happens when students in the same country learn markedly different versions of its past? This article examines divergent narratives in French- and English-language history curricula across six Canadian provinces outside Québec. Drawing on a comparative analysis of Grades 7–11 curriculum documents and critical discourse analysis of key expectations and rationales, it identifies persistent asymmetries in how francophone minority histories are represented… or omitted. French-language curricula tend to foreground resistance, community survival, and political agency, whereas English-language curricula frequently marginalise or dilute episodes such as the Conquest (1759), Regulation 17, and the legacy of Louis Riel. These contrasts are not merely lexical; they organise different distributions of agency, responsibility, and visibility, with significant consequences for how students learn to imagine who belongs to the national “we”. To capture this structural imbalance, the article develops the concept of narrative asymmetry in bilingual curriculum ecosystems and argues that such curricular inequity undermines both bilingualism and civic pluralism. The discussion then explores the identity and pedagogical implications of these asymmetries, showing how they shape francophone and anglophone students’ sense of recognition, legitimacy, and historical understanding. The article concludes by outlining avenues toward narrative equity in history education through curriculum reform, teacher education, and historical-thinking pedagogy, and suggests how this framework might be adapted to other multilingual societies grappling with tensions between official narratives and marginalised histories.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
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 head0.109
GPT teacher head0.460
Teacher spread0.351 · 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 designObservational
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 routes1
Has abstractyes

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