Historical Empathy: A Cognitive-Affective Theory for History Education in Canada
Bibliographic record
Abstract
Historical empathy involves a process of attempting to understand the thoughts, feelings, experiences, decisions, and actions of people from the past within specific historical contexts. Although historical empathy has been a rich area of study in history education for several decades, this research has largely taken place outside of Canada. In this article, I argue that greater attention should be paid to historical empathy in Canadian history education research and curriculum because it can support learning outcomes related to historical thinking and historical consciousness, citizenship, and decolonizing and anti-racist approaches to history education. Drawing from and commenting on other scholarship, I present a cognitive-affective theory of historical empathy which includes five elements: (1) evidence and contextualization, (2) informed historical imagination, (3) historical perspectives, (4) ethical judgements, and (5) caring. Through exploring each element and some pedagogical considerations for educators, I emphasize the affective dimensions of history to centre their importance for history education in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".