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Record W2032983709 · doi:10.7202/1025923ar

One of Us? From Bad Taste to Empathy. Otherness in Contemporary Hollywood Movies

2014· article· en· W2032983709 on OpenAlexvenueno aff
Adrienne Boutang

Bibliographic record

VenueRecherches sémiotiques · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHollywoodComicsNormalityTasteEmpathyDramaMovie theaterAestheticsRepresentation (politics)PsychologyVulnerability (computing)JokeSociologyCharacter (mathematics)Social psychologyArtLiteratureComputer scienceLawPolitical science

Abstract

fetched live from OpenAlex

This article aims to examine the way contemporary Hollywood cinema deals with the topic and the visual representation of disability. Its goal is to highlight the way social recognition of vulnerability and the requisite sensitivity involved in dealing with vulnerable bodies, have influenced recent “gross-out” comedies. In a way that is very different from the famous drama Freaks, recent comedies take into account the fine line between normality and difference, and use disability as a comic trick to question the viewer’s automatic responses to physical difference. Thus, what at first appears to be bad taste, both on an aesthetic and on an ethical level, turns out to be a clever attempt to get past the boundaries between normality and disability, and present vulnerability as a universal condition. The use of gross-out humor, and of vulgar body genres, therefore works as a trigger, calculated to disrupt boundaries and challenge classical representations of physical otherness.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0080.005
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.186
GPT teacher head0.303
Teacher spread0.118 · 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 designQualitative
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
Published2014
Admission routes1
Has abstractyes

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