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Record W7137902689 · doi:10.5038/1911-9933.19.2.2075

Guiding Principles for Teaching about Genocide and Other Difficult Pasts

2025· article· W7137902689 on OpenAlexvenueno aff
Kerry E Whigham

Bibliographic record

VenueGenocide Studies and Prevention · 2025
Typearticle
Language
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideTransformative learningHuman rights educationHuman rightsAgency (philosophy)CitizenshipThe HolocaustCitizenship educationPoliticsStructural violence

Abstract

fetched live from OpenAlex

This article outlines guiding principles for teaching “difficult pasts”—episodes of large-scale, identity-based violence such as genocide, crimes against humanity, colonialism, and enslavement—in ways that promote peace, human rights, and the prevention of future atrocities. Drawing from the fields of Holocaust and Genocide Education (HGE), Global Citizenship Education (GCED), Peace Education (PE), and Human Rights Education (HRE), it argues that instruction should move beyond the memorization of historical facts toward developing students’ socio-emotional and behavioral capacities to recognize and resist processes of othering, discrimination, and violence. The brief presents three main areas of guidance: curricular content, emphasizing conceptual understanding over rote knowledge and the exploration of violence as a process; pedagogical methods, advocating for interdisciplinary approaches, active learning, and schools as inclusive civic communities; and educational policy, highlighting the need for teacher training, mandated curricula, and integration within broader human rights and peace frameworks. Recognizing the political sensitivities of confronting local histories, the brief proposes multiple levels of engagement—from local to international cases—allowing educators to tailor content to context. Ultimately, teaching difficult pasts offers a transformative opportunity to equip young people with the knowledge, empathy, and agency needed to build more just and peaceful societies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.481
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.098
GPT teacher head0.401
Teacher spread0.303 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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