Deliberating and Learning Contentious Issues: How Divided Societies Represent Conflict in History Textbooks
Bibliographic record
Abstract
Abstract History education can either exacerbate polarization and division or it can have conciliatory potential. Looking at a number of divided societies, we identify trends in curriculum portrayals of inter‐group conflict. Noting the power of re‐telling the past, we argue for a conciliatory approach to textbook design that entails the inclusion of multiple narratives. We detail why groups need to set out their own account of events and discuss the importance of the way that groups develop their accounts. We recommend an institutional, process‐based approach to textbook design grounded in the values of deliberative consociationalism and argue that the conciliatory approach is best pursued in a two‐stage model of deliberations. We develop this model and focus on how deliberations might occur and with what restrictions, taking seriously concerns about the applicability of deliberation in divided societies.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".