The Affective Dimensions of Historical Empathy: Opportunities, Problems, and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emotions and feelings play an important role within history education. Yet, the affective dimensions (feelings, emotions, connections) of learning about the past are understudied within research on historical empathy—defined here as a cognitive-affective process of attempting to understand the thoughts, feelings, experiences, decisions, and actions of people from the past within their historical contexts. Drawing from interviews with secondary school history teachers in Canada, this article offers insight into teachers’ perspectives on constructive ways that they approach the affective dimensions within history classrooms, as well as problems and challenges that arise when they intentionally elicit emotions or encounter them unexpectedly. In doing so, the article aims to further conceptualize the affective dimensions of historical empathy and expand understandings of emotions in history and social studies education, while positioning these discussions in relation to history education in Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it