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Record W2790052916 · doi:10.5539/ells.v8n1p45

What Iago Knew

2018· article· en· W2790052916 on OpenAlexvenueno aff
Roberto Gigliucci

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVerisimilitudeAntithesisPhilosophyLiteratureArt

Abstract

fetched live from OpenAlex

This paper defines Iago as a master of time. He knows the future, or, even better put, he is able to foresee it quite brilliantly. Such an ability is typical of a Melancholy character, which, as known, can be a veritable villain. Iago instinctively knows that Desdemona will come to grow weary of the Blackamoor, and he detects her attraction to the young, handsome, and white Cassio. As head and meta-theatrical director, Iago sets out to compress time, and so exert pressure on the other characters. As a result, what would normally take place over a longer stretch of time, becomes quickly contracted in the space of a play. Moreover, considering how the ‘future’ is brought forward, the present appears more ambivalent. From Iago’s point of view, is Desdemona a potential or an inevitable adulteress? To think the worst is, for the villain, to think realistically. Seeing time as following the rules of trivial consistency and verisimilitude (rendering the future predictable), makes it perfectly natural for Iago to consider Desdemona as an unfaithful woman, and Cassio, a coxcomb who plays around with other men’s wives. Furthermore, the Moor is Black, and despite his “fairness”, he will soon become a bad Negro again. Time will prove me right, Iago meditates. Thus, he zips time to triumph further and faster. The last section of the essay is dedicated to the occurrences of the word time in the play, with specific commentaries under the shadow of the secular exegesis, and in line with the critical assumptions made. Finally, in the discussion, the darker side of Iago is also explored, with careful assessment of the extensive bibliography on the subject.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.534

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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 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
Published2018
Admission routes1
Has abstractyes

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