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Record W2050800514 · doi:10.4000/shakespeare.1710

Dangerous Feasts and the Social Appetite in Macbeth and Titus Andronicus

2012· article· fr· W2050800514 on OpenAlexaff
Natalia Brzozowska

Bibliographic record

VenueActes des congrès de la Société française Shakespeare · 2012
Typearticle
Languagefr
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsHumanitiesArtBanquetPhilosophyArt history

Abstract

fetched live from OpenAlex

Les banquets ont toujours été perçus comme une occasion festive. Cependant, quelques scènes de banquets célèbres dans les pièces de Shakespeare vont sciemment à l’encontre de ce stéréotype. La sociologie envisage les repas festifs comme le lieu de la consolidation des liens personnels, assurant l’ordre social et permettant à chacun d’afficher légitimement son statut. Néanmoins, dans Macbeth, deux banquets conduisent les personnages à leur perte. Au cours du premier, le choix de Duncan de se conformer à son rôle d’hôte, suivant les règles socio-anthropologiques de l’hospitalité, ainsi que son meurtre sont présentés de manière similaire afin de terrifier les spectateurs. Au cours du second, la dramaturgie est rompue, ce qui souligne l’incapacité de Macbeth de faire face à son nouveau rôle de monarque. En offrant à Tamora le cœur de ses enfants, Titus Andronicus accomplit un acte scandaleux parodiant les repas familiaux. Le banquet final ne peut que finir en carnage. Ces trois exemples seront analysés à travers le prisme de la sociologie, à l’aide de la théorie des rôles (de Linton à Goffman, en prenant en compte les dernières études sur le rôle social), de certaines théories récentes sur la nourriture et la nutrition, ainsi que des théories plus anciennes sur la déviance (Becker, Sutherland, Znaniecki).

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0130.016
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.028
GPT teacher head0.354
Teacher spread0.326 · 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 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
Published2012
Admission routes1
Has abstractyes

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