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Record W4308203165 · doi:10.18357/tar131202220764

Stücke: Graphic Vignettes and the Haptic Response in László Nemes’s Saul fia

2022· article· en· W4308203165 on OpenAlexaffvenue
Sarah D. Wald

Bibliographic record

VenueThe Arbutus Review · 2022
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGazeThe HolocaustHaptic technologyExtant taxonPerspective (graphical)Shot (pellet)Frame (networking)Shadow (psychology)Focus (optics)AestheticsArtVisual artsPsychologyComputer sciencePhilosophyPsychoanalysisArtificial intelligenceOptics

Abstract

fetched live from OpenAlex

This article examines László Nemes’s film Saul fia (2015) and its visual and sonic use of brutalized human bodies to induce a haptic response in its audience. Presented out of focus and often at the periphery of the shot, the visual frame of human death causes viewers of the film to recoil into the centre of the frame—a space typically occupied by the film’s protagonist, Saul Auslӓnder. This article argues that these visually and sonically induced haptic triggers, and the claustrophobia that results, tether the audience to Auslӓnder to re-centre the viewer’s gaze as that of a companion rather than perpetrator. Drawing from extant literature on cinematic haptics and filmic representations of the Holocaust, this article engages with contemporary discourses on graphic Holocaust representations in contemporary feature-length films to examine the impact of graphic imagery and haptic cinematics on the perspective and ability of the viewer to subvert the perpetrator gaze.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.320
Teacher spread0.286 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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