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Record W4402035430 · doi:10.1080/00933104.2024.2396319

The Nakba in Israeli history education: Ethical judgments in an ongoing conflict

2024· article· en· W4402035430 on OpenAlexaff
Roy Weintraub, Lindsay Gibson

Bibliographic record

VenueTheory & Research in Social Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of British Columbia
FundersHORIZON EUROPE Marie Sklodowska-Curie Actions
KeywordsPsychologySocial psychologyPolitical scienceEnvironmental ethicsPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.417
GPT teacher head0.589
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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