Teaching history: A discussion of contemporary challenges
Bibliographic record
Abstract
The authors argue that intellectual shifts and related ideological debates have set new pedagogical demands on history teachers and new programmatic demands on faculties of education. In an attempt to relate the relevance of generating historical thinking (motivating the students to think like historians) to transformative education, the authors outline an history inquiry model based on Dewey’s educational theory. In this model, content knowledge and mastery of the subject matter is as critical as an understanding of teaching and learning history. The paper addresses the challenges set by a dominant relativist self-referential slant, the teaching of history in a multicultural class, and the tendency, in particular in social studies classes, to fall into presentism.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.022 | 0.050 |
| Scholarly communication | 0.029 | 0.030 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.021 | 0.017 |
| 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".