Genocide Education is not the Study of 'Gloom and Doom and Terror': Investigating Experiences of Genocide Educators in Ontario Secondary Schools
Bibliographic record
Abstract
Current research and professional practice suggests that genocide education can help students question the historical and moral implications of human rights violations, explore their own identities in relation to crimes against humanity, and become more engaged, responsible citizens. However, the material explored in genocide education can be emotionally taxing on a personal level and challenging for teachers to effectively convey to students. While there has been an increase in course development related to genocide education at the secondary level in Ontario, there is little research that supports educators with effective teaching strategies, methods, and tools. This study documents some of the challenges that Ontario secondary school educators encounter when teaching genocide education. Participants for this study were recruited because of their experiences teaching genocide education in Ontario through the Grade 10 Canadian History and the Grade 11 Genocide and Crimes Against Humanity courses. Findings of this study uncover suggestions on how best to support teachers and students when engaging with genocide material. These findings suggest that educators often feel uncomfortable teaching genocide material, which translates to challenges in implementing critical pedagogy in the classroom, among the others. In order to address this challenge, genocide educators need to be supported through better awareness of professional development opportunities and outside resources.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".