Addressing the elephant in the room: Ethics as an organizing concept in history education
Bibliographic record
Abstract
Ethics is the proverbial ‘elephant in the room’ in history education in settler-colonial nations. It is foundational to teaching and learning history and engaging with the ongoing effects of the past in the present. Yet its place in history curricula and teaching continues to be ignored, understated, confused, and challenged. This article illustrates how ethical judgment is central to four commonly identified rationales for teaching history in schools: citizenship education, historical consciousness, historical thinking, and difficult histories. The article urges more explicit attention to ethics as an organizing concept in history education to enable students to appreciate the complex lived realities that constitute history and to explore the diverse perspectives that have contributed to sometimes-difficult decisions. We argue that ethics can humanize history, enrich students’ historical understandings, and offer a usable past. However, given the varied approaches to ethical judgment across the four orientations to teaching history, we stress the need for the mindful deployment of ethical judgment in curriculum design. Using an example from the 2021 draft Aotearoa New Zealand’s Histories curriculum, we demonstrate what “ethical judgment” could be called upon to do, and the impoverished approach to history education that would exist without it.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".