The relationship between past and history in teachers’ theoretical understandings and professional practice
Bibliographic record
Abstract
Perceived inconsistencies in history teachers’ epistemological beliefs have been a recurring theme in research on epistemic cognition. In this article, we explore how teachers discuss history’s epistemology when presented with different scenarios where epistemology might be an issue. A study design aimed at capturing teachers thinking in different contexts was adopted, and through semi-structured interviews with history teachers in Quebec and Sweden we could follow changes in nuance by analyzing teacher statements in relation to their ideas about the relationship between the past itself and the (teachable) history about the past, when discussing these issues in relation to different scenarios. The results point to teachers articulating rather well-adjusted and consistent epistemological beliefs when discussing the matter at a theoretical level while tending to adapt these beliefs—probably for pedagogical and practical reasons—when they discuss their own teaching and specific classroom situations. We argue that the teachers rarely seem to be notice the changes in their epistemological reasoning, but changes tend to go from complicated thought to more straightforward when complexity in context increases.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".