The Nakba in Israeli history education: Ethical judgments in an ongoing conflict
Bibliographic record
Abstract
The Nakba, which means “the catastrophe” in Arabic, is the most controversial historical topic in Israeli history education. Despite the Nakba’s significance to the history of Israel and the ongoing Israeli-Palestinian conflict, until the last decade it has traditionally been excluded from Israeli public discourse and school curriculum. This article analyzes the different types of ethical judgments about the Nakba included in authorized curricula, teaching resources, and national exams currently used in the Israeli Jewish public education systems. Our data analysis reveals that the Nakba is explicitly mentioned in 40% of teaching materials used in Israeli Jewish schools, and the teaching materials include six types of implied ethical judgments. We developed a typology of three ethical justifications commonly utilized in the teaching materials: denial; acknowledging suffering, limited responsibility; and complex engagement. While some of the teaching materials promote critical engagement with the Nakba, others continue to deny its existence or minimize the negative consequences experienced by Palestinians. This research highlights how political beliefs influence the ethical judgments made in teaching materials and the importance of teaching about ethical judgments for helping students critically engage with and understand difficult histories.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".