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Record W4388125291 · doi:10.25071/1916-4467.40756

Autochtoniser l’enseignement de l’histoire du Québec et du Canada au secondaire et à l’université : entre volonté, obstacles épistémologiques et tensions identitaires

2023· article· fr· W4388125291 on OpenAlexaffvenueabout
Sabrina Moisan, Aude Maltais-Landry

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversité LavalUniversité de Sherbrooke
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

L’appel à l’action 62 de la Commission de vérité et réconciliation (CVR) du Canada (CVR, 2012), qui porte sur l’éducation pour la réconciliation, interpelle directement les institutions scolaires canadiennes, et plus précisément la classe d’histoire. Alors que l’éducation des enfants non-Autochtones est prise en charge par les provinces, il semble que le Québec avance plus lentement que d’autres provinces dans le projet de reconnaissance et d’inclusion des perspectives et des savoirs autochtones dans l’enseignement de l’histoire. Comment expliquer cette spécificité de l’histoire coloniale du Québec? Pour y répondre, nous avons questionné des personnes enseignant l’histoire du Québec et du Canada au secondaire et à l’université (n=46) sur leur position à l’égard de l’inclusion des savoirs et des perspectives autochtones dans l’enseignement de leur discipline et les pistes d’action qu’elles identifient pour mettre en œuvre les appels à l’action de la CVR. De cette analyse, nous constatons d’abord un certain engouement pour l’inclusion de l’histoire autochtone dans les cours, mais se dégagent également des obstacles épistémologiques et identitaires pouvant expliquer la spécificité québécoise dans le projet d’autochtonisation en cours au 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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.703

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0270.022
Scholarly communication0.0110.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.036
GPT teacher head0.302
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the Canadian Association for Curriculum StudiesSame topicEducator Training and Historical PedagogyFrench-language works237,207