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Narratives in Pencil: Using Graphic Novels to Teach Israeli-Palestinian Relations

2010· article· en· W1596206254 on OpenAlexaff
Thomas Juneau, Mira Sucharov

Bibliographic record

VenueInternational Studies Perspectives · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsCarleton University
Fundersnot available
KeywordsNarrativePalestineShadow (psychology)TerrorismIdentity (music)WaltzSociologyPoliticsVisual artsLiteratureMedia studiesPsychologyPolitical scienceLawHistoryAestheticsPsychoanalysisArt

Abstract

fetched live from OpenAlex

This article argues that using graphic novels is an effective and valuable pedagogical tool to enhance the teaching of international relations, and specifically the Israeli-Palestinian conflict. Graphic novels combine the best of film and prose in delivering a cognitive and affective experience that allows students to access the subject matter in a manner that complements the use of more conventional textbooks. Three such novels—Palestine, by Joe Sacco (2001), Exit Wounds, by Rutu Modan (2007), and Waltz with Bashir, by Ari Folman and David Polonsky (2009)—raise a number of important and relevant themes such as life under occupation and the shadow of terrorism, the intractability of conflict, the sources of violence, tensions within Israeli society, and collective memory and identity. After reviewing these three novels, this article discusses the benefits and challenges associated with using graphic novels in the political science classroom.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.005
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.177
GPT teacher head0.477
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations25
Published2010
Admission routes1
Has abstractyes

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