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Record W2312563960 · doi:10.17742/image.scandal.4-1.2

BEAUTIFUL JUNKIES: IMAGES OF DEGRADATION IN REQUIEM FOR A DREAM

2013· article· en· W2312563960 on OpenAlexvenueno aff
Renée R. Curry

Bibliographic record

VenueImaginations Journal of Cross-Cultural Image Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican and British Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDreamArtMonsterSobrietySolitudeLiteraturePower (physics)Character (mathematics)PsychoanalysisArt historyPsychologyPhysics

Abstract

fetched live from OpenAlex

In Darren Aronofsky’s 2000 film, Requiem for a Dream, based on Hubert Selby Jr.’s 1978 novel, he depicts extreme close-up images of heroin as it cooks, boils, enters a vein, and then passes into the body at the cellular level. The cells sizzle as heroin numbs them. The close-ups and sizzling sounds repeat themselves more and more frequently as our four main characters disintegrate through the process of becoming junkies. These images and others provide vivid, horrific, and exquisite visual renderings of the addiction process, while simultaneously providing stark evidence of heroin’s take-over of the body, mind, and ethical capabilities. The images of heroin’s allencompassing control of the body at its foundational level do not glorify heroin’s power in Aronofsky’s film; these images serve as documents of pure horror. The degradation is devastating, thorough, real, and scarring. Aronofsky describes his film as a monster movie, a modern horror film. And, it is not the type of film in which redemption occurs. The stark and individual solitude of each character at the end of the film cannot be easily penetrated by sobriety or love anytime in the foreseeable future.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0010.010
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.371
Teacher spread0.337 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

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