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Record W3042117110 · doi:10.24908/iqurcp.14053

Categorizing Identity: Literary, Performative, and Legal Languages in Oscar Wilde’s Libel Trial vs the Marquess of Queensberry

2020· article· en· W3042117110 on OpenAlexvenueno aff
Fred Hook

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPerformative utteranceConvictionIdentity (music)Relation (database)LawHomosexualityLiteratureHistorySociologyAestheticsArtGender studiesPolitical science

Abstract

fetched live from OpenAlex

Wilde has been generally accepted as one of the first figures of the queer man that is now known, however, how he came to represent this identity has not been much discussed. While his criminal trials, which led to his eventual conviction of ‘gross indecency,’ undoubtedly played a strong part on his emerging portrayal as a gay man, his first trial involving a libel suit against the Marquess of Queensberry is little discussed in relation to the start of his downfall and portrayal as a gay man. Thus, this project looks at Oscar Wilde’s libel trial and its effects on the identity of the homosexual man. By looking at the language used in the libel trial and its use of The Picture of Dorian Gray as evidence, the project concludes that by using the interpretations of Wilde’s novel during the trial, the law created a concrete image of what ‘gross indecency between men’ was, and of the type of person who participated in it, using Wilde as a representative for this identity. The way that his identity was forged allows us to see that while homosexuality as a way of being began to take shape thanks to Wilde’s trial, it was still imbued with negative connotations and seen as pederastic, tying it to anxieties around child prostitution and trafficking of the 1800s. The development of this new identity and its portrayal betters the understanding of the vilification of Wilde during his downfall and his novel’s role in this.

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 categoriesnone
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 score0.537
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.347
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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