Difficult Knowledge and Alternative Perspectives in Ontario's History Curriculum
Bibliographic record
Abstract
This study used qualitative research methods to analyze the ways in which difficult knowledge is represented in Ontario’s 2013 and revised 2018 history curriculum (Grades 7, 8, 10). Difficult knowledge promotes serious discussions about weighty topics – often entrenched in collective memory – and invites readers to reflect on the different values, beliefs, and perspectives around such topics. In this study, difficult histories refer to contested depictions of past violence and oppression as they appear in historical narratives and curricular frameworks (Epstein and Peck, 2017). Examining the curriculum using the lens of difficult knowledge allowed me to consider how educators might foster reconciliation through engagement with chapters in Canadian history. The content analysis considered the difficult knowledge topics in history curricula and the approaches proposed to encourage perspective-taking. The study used a critical sociocultural approach to explore how Ontario’s official curriculum represents difficult knowledge using multiple perspectives in general, and Indigenous perspectives, specifically. In an effort to gain a better understanding of the curricular resources currently available, this study contributes to knowledge growth by identifying entry points in the curriculum that serve to help teachers introduce difficult knowledge using disciplinary thinking and Indigenous epistemic themes. The main goal with this research is to provide recommendations to guide policy, research, and practice in the integration of Indigenous perspectives and knowledges in ways that are meaningful to learners.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".