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Record W4385647974 · doi:10.1515/9781474492850

Martialling Peace

2023· book· en· W4385647974 on OpenAlexaboutno aff
Nicole Wegner

Bibliographic record

VenueEdinburgh University Press eBooks · 2023
Typebook
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

The peacekeeper—impartial, disciplined, helpful and restrained in their lethal capacity—is a powerful trope. This book examines the mythology of international peacekeeping and focuses on Canada as a case study of a "peacekeeper par excellence" (Jockel, 1994) and the ways the peacekeeping myth both challenged and condoned combat activities in Afghanistan between 2001 and 2014. While Afghanistan was explicitly not a peacekeeping mission, peacekeeping mythology circulated throughout discourse about the War. The book examines the salience of the peacekeeping myth throughout twelve years of Canadian combat activities in Afghanistan as a means to illustrate the adaptability and political utility of this (inter)national myth. It examines how gender, militarism, and nationalism operated in political discourse through the War in Afghanistan to justify military force and violence in the name of peace. The book draws on the Canadian case to address a broader set of questions related to how militarism, gender, and national myths are co-constitutive in condoning military violence of so-called "peaceful" liberal nations.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: Other
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0520.015

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 head0.092
GPT teacher head0.228
Teacher spread0.136 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations3
Published2023
Admission routes1
Has abstractyes

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