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Record W3155055994 · doi:10.24908/iqurcp.10536

“L’enfer, c’est les autres”: Photographic Representations of Character Development in Sartre’s Huis clos

2018· article· en· W3155055994 on OpenAlexvenueno aff
Daniel Habashi, Shannon Hogan, Victoria Wolf

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsnot available
Fundersnot available
KeywordsTortureCharacter (mathematics)ArtTheme (computing)Representation (politics)Presentation (obstetrics)FocalizationArt historyLiteraturePhilosophyHumanitiesPoliticsNarrativeLawComputer science

Abstract

fetched live from OpenAlex

Huis clos (No Exit), a play by Jean-Paul Sartre, is an unconventional representation of Hell, where three individuals torture one another in a Second Empire style lounge complete with a butler, a servant bell, and extravagant furniture. Estelle, Inès, and Garcin are eternally inseparable, and their non-violent manner of torture - namely, the manipulation of each other’s earthly insecurities - inspires the famous quote “L’enfer, c’est les autres” (“Hell is other people”). In our presentation, we will explore the possibility of transposing this famous play in a different artistic medium.
 As an alternative medium to theatre, photography uses instantaneous frames to capture the complex details of character relations and theme without deviating too far from the play’s original form. Our work consists of a series of three photographs that use symbolic objects and body language to illustrate each character’s unique version of Hell. When taken both individually and as a whole, these photos represent a cycle of mutual torture and mutual dependence, and highlight the ways in which the characters’ past lives influence the form that torture takes in the afterlife. Parts of this presentation will be in English, and components requiring textual analysis will be done in French.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.379
Teacher spread0.217 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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