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Record W1884374358 · doi:10.1111/rest.12023

<i>The Merchant of Venice</i> and <scp>S</scp>hakespeare's sense of humour(s)

2013· article· en· W1884374358 on OpenAlexaff
J. F. Bernard

Bibliographic record

VenueRenaissance Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComedyComicsLaughterPerformative utteranceLiteraturePlot (graphics)RomanceCognitive dissonanceArtReading (process)AestheticsPhilosophyPsychologySocial psychologyLinguistics

Abstract

fetched live from OpenAlex

This paper examines a crucial and often overlooked dimension of The Merchant of Venice as it pertains to both its performative and critical histories; namely, how the presence of a melancholic character problematizes the play at its core. This essay argues that the tonal dissonance created by Antonio's melancholy represents an integral component of its comic progression and, in doing so, construes the play as representative of S hakespeare's idiosyncratic brand of comedy. I maintain that, in relying on a melancholic protagonist who defies comic classification – much like the play itself – the comedy eschews both the Galenic understanding of humoural theory, predicated on an imbalance of bodily substance, as well as the Jonsonian style of humour plays, where each absurd affect is expected to yield mockery and laughter. Rather than consider Antonio's melancholy as a critical springboard, however, I discuss its impact within the comedy it inhabits, reading the merchant as the dramatic intersection through which the play's multiple plots and characters converge. Though he is in no way the play's antagonistic figure, Antonio represents a considerably dissonant note that jeopardizes the concretization of the romantic plot, hence his ostensible exclusion at the end of the comedy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.044
GPT teacher head0.256
Teacher spread0.213 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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