Understanding Teachers’ Experience with a Revised History Curriculum
Bibliographic record
Abstract
Quebec’s mandated high school history course has received much public attention since the reimagining of the curriculum in 2006. In this thesis, I examine the historical contexts and debates surrounding Québec’s mandated history courses published in 1982, 2006 and 2016. I investigate the problems with the 2006 curriculum and conduct a policy analysis of the circumstances for replacing the 2006 curriculum only 10 years after its publication. The 2016 curriculum is based on the recommendations of a public consultation by Beauchemin and Fahmy-Eid (2014). A policy analysis of these recommendations reveal that Beauchemin and Fahmy-Eid tightly controlled text and discourse in favour of a national historical narrative. Consequently, the Beauchemin and Fahmy-Eid report (2014) limits Quebec’s minority and marginalized communities from connecting to or being validated by history. The 2016 version of the curriculum has come under scrutiny for overemphasising the historical contributions of one nation through a unique Québec lens (Bradley & Allison, 2021). The phenomenological portion of my thesis shows that teachers of English-speaking students in Québec are aware that the 2016 curriculum does not validate minority and marginalized communities and accommodate the curriculum by delivering the material in ways that undermine the nationalistic and civic aspects of the curriculum and encourage students to reflect on their place and that of others in Québec’s social culture.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".