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Record W2020086457 · doi:10.1525/si.2009.32.1.1

Strategic Role Taking and Political Struggle: Bearing Witness to the Iraq War

2009· article· en· W2020086457 on OpenAlexaboutno aff
Eric Bonds

Bibliographic record

VenueSymbolic Interaction · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsWitnessCognitive reframingPoliticsMainstreamNarrativeDehumanizationSociologyMedia studiesPeace movementLawPolitical scienceSocial movementGender studiesSocial psychologyPsychology

Abstract

fetched live from OpenAlex

U.S. and Canadian peace activists traveled to Iraq as a social movement tactic, in the buildup to the war and during the war itself, in an attempt to sustain or increase peace activism at home. Based on interviews with fourteen peace activists, this study analyzes how the presence of antiwar activists in Iraq serves two social movement goals. First, their presence in Iraq bestowed activists increased access to media, bolstering their ability to reframe the war within mainstream media accounts. Second, by traveling to Iraq, activists furnished themselves with stories of the hardships and suffering of war to share with audiences at home. By retelling these narratives, activists provide opportunities and obligations for audience members to imaginatively take the role of Iraqi civilians, in the hope that audience members will practice moral reasoning and be consequently moved to act against the war. To provide these role‐taking opportunities, peace activists must also engage in a political struggle over “otherhood” by countering official attempts to dehumanize Iraqis.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0290.021
Scholarly communication0.0070.003
Open science0.0010.006
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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 designQualitative
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

Citations8
Published2009
Admission routes1
Has abstractyes

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