Education as Reconciliation?: Unpacking the Relationship between Non-Indigenous Canadian/Indigenous Reconciliation and Ontario Secondary School Canadian History Textbooks
Bibliographic record
Abstract
At the present moment, Canada is at a crossroads. Canada claims to be promoting the restoration of non-Indigenous Canadian/Indigenous relations in an effort to heal from the trauma of its settler colonial past (while limiting recognition of the present consequences of this history). This project, guided by a decolonizing framework, conducts a content analysis of four Canadian history textbooks published for Ontario high schools from 2000-2014 and the associated Canadian history curriculum guidelines for grades nine/ten and eleven/twelve students from 2000-2018. I ask if these history textbooks supported by the Ontario Ministry of Education facilitate students to be interested in reconciliation efforts, particularly looking the attitudes towards Indigenous peoples and the ways various settler colonial events are explained. This research has shown that the Canadian history textbooks used in conjunction with these curricula act as both an encouragement and discouragement to reconciliation through both the content that is taught (or not) and the language used surroundings the subjects covered. As these textbooks and curricula evolve and reflect more of the Truth and Reconciliation Commission of Canada’s Calls to Action, particularly Calls 62-65, there appears to be greater opportunities for students to discuss Indigenous issues, their roles in these issues, and ultimately, their roles and responsibilities in reconciliation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".