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A Gash in the Portrait

2013· reference-entry· en· W2731178894 on OpenAlexaff
George Toles

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNarrativeArtPathosArt historyPortraitKey (lock)LiteratureVisual artsComputer science

Abstract

fetched live from OpenAlex

This article appears in the Oxford Handbook of Sound and Image in Digital Media edited by Carol Vernallis, Amy Herzog, and John Richardson. This essay examines the effects of image and sound manipulation in Martin Arnold’s 2002 installation, Deanimated. Austrian experimental filmmaker Arnold has digitally reworked a B-horror movie starring Bela Lugosi, Joseph Lewis’s The Invisible Ghost (1941). Taking the horror movie’s title literally, Arnold has excised, in a gradual fashion, all of the characters in the original narrative from the film’s images, as well as their dialogue and the sounds of their actions. The viewers of Deanimated are left to contemplate the interior of the house that is The Invisible Ghost’s main setting, rooms that are to a palpable degree haunted by the suppressed narrative and the not quite vanished presences of those who had enacted it. The essay explores the logic of this “remnant” scenario of mourning and how it reasserts themes from the host narrative in a different key. The Invisible Ghost gains both fresh dimension and pathos from the slow ravaging of its contents.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.052
Threshold uncertainty score0.174

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 studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0520.021

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 head0.053
GPT teacher head0.232
Teacher spread0.179 · 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
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

Citations0
Published2013
Admission routes1
Has abstractyes

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