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Record W2803372802 · doi:10.3138/jcs.2017-0025.r1

The Elsewhere War: Art, Embodiment, and the Spaces of Military Engagement

2018· article· en· W2803372802 on OpenAlexvenueaboutno aff
Susan Cahill

Bibliographic record

VenueJournal of Canadian Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsAestheticsEmbodied cognitionContext (archaeology)NarrativeSociologyMeaning (existential)Media studiesHistoryVisual artsArtEpistemologyLiteratureArchaeology

Abstract

fetched live from OpenAlex

This article examines embodiment as a strategy of meaning-making that can generate alternative narratives about Canada’s participation in the War on Terror. It takes as its point of entry Canadian artist Barb Hunt’s series Camouflage, which adapts worn camouflage military fatigues into artworks. The three pieces from this series discussed here—Incarnate, Fodder, and The Old Lie—make material the bodily forms of war to Canadian museum-goers in order to challenge the perceived disconnection of Canadian civilians from wars fought in the spaces of the Canadian elsewhere (i.e., outside of and remote from the “home” space of Canada). This paper examines Hunt’s practice as enabling a context for an embodied encounter that activates a critical and empathic engagement. Specifically, it explores the intersections of art, embodiment, and critical empathy as a provocation that can disrupt familiar ways of being-in-the-world, particularly in relation to the sense of war as occurring in a dislocated and distanced elsewhere. In this way, the intersection of Hunt’s art and embodied knowledges provides new insights into thinking through Canadians’ relationship to and implication in ongoing military activities occurring elsewhere.

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)0.002
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: none
Teacher disagreement score0.355
Threshold uncertainty score0.714

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0210.061
Scholarly communication0.0130.005
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.306
Teacher spread0.274 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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