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Record W4392409732 · doi:10.1093/oxartj/kcad030

Rescue Politics: Richard Mosse’s Thermal Imaging and the Containment of Migration

2023· article· uk· W4392409732 on OpenAlexfundno aff
Sarah Bassnett

Bibliographic record

VenueOxford Art Journal · 2023
Typearticle
Languageuk
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContainment (computer programming)PoliticsPolitical scienceSociologyComputer scienceLaw

Abstract

fetched live from OpenAlex

This article focuses on a body of work by Mosse that includes the multichannel video installation Incoming, shown as a 52:12 minute three-channel video installation with 7.3 surround sound, and the photographic series Heat Maps (2015–2017) (Fig. 1). While Incoming concentrates on migration routes, Heat Maps portrays the architecture of refugee camps.4 I consider how the artist’s use of thermal imaging and his immersive mode of documentary complicates tropes used to represent migration. I reflect on the way Mosse’s artwork intersects with a long-standing interest by practitioners and theorists of film and photography in the idea of the camera as a tool that extends vision, and in this way, how it takes up Jacques Rancière’s claim regarding the political significance of an aesthetic endeavour as its ability to make visible what is not normally recognised.5 I differentiate my approach from discussions that deal with forced migration in terms of the concept of bare life and migration zones as spaces of exception.6 This framework, which borrows from philosopher Giorgio Agamben, tends to establish an opposition between subjects with and without rights, disconnecting migrants from the complex problems that have caused their displacement. Instead, taking a cue from Lilie Chouliaraki and Tijana Stolic, I consider how Mosse’s representation of involuntary migration offers insight into the political failures that have led to migrant precarity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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