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Record W4413902077 · doi:10.1080/17400201.2025.2548201

Implications of articulating peace education with Islam: an analysis of Afghanistan public school textbooks

2025· article· en· W4413902077 on OpenAlexaff
Noorin Nazari

Bibliographic record

VenueJournal of Peace Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIslamIslamic culturePeace educationPedagogyReligious educationSociologyPsychologyPolitical scienceMathematics educationHistory

Abstract

fetched live from OpenAlex

This study analyzes the articulation of peace with Islamic teachings in public school textbooks in Afghanistan by answering two questions: How is peace represented in the patriotism and civic education textbooks issued by the Ministry of Education of the Islamic Republic of Afghanistan? And, how is peace articulated with Islam within these textbooks? The study identifies three primary findings: First, while the textbooks briefly reference the concept of peace, their primary focus is on security and the threats posed by insecurity. Second, at the national level, the textbooks articulate peace education with a moderate interpretation of Islam that promotes positive social values. Third, concerning Afghanistan’s relationship with the Western world, the textbooks articulate peace with a confrontational Islam, one that asserts the superiority of Islamic thought over Western ideologies, condemns the economic, military, and cultural dimensions of globalization as a civilizational clash between the Islamic and Western worlds, and calls for jihad and martyrdom. This study demonstrates how each of these conceptual constructions are ruptured due to internal conflicts but negotiated into forced unities. Utilizing critical discourse analysis, the integrative theory of peace, and the concept of articulation, this content analysis examines textbooks published prior to the Taliban’s 2021 takeover.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.373
Teacher spread0.351 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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