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Record W2727646269 · doi:10.33137/rr.v37i1.21284

“Worthy my blood”: Inheritance, Imitation, and Gendered Familial Emotions in John Marston’s <i>Antonio</i> Plays

2014· article· en· W2727646269 on OpenAlexvenueno aff
Megan Elizabeth Allen

Bibliographic record

VenueRenaissance and Reformation · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicFoucault, Power, and Ethics
Canadian institutionsnot available
FundersAndrew W. Mellon Foundation
KeywordsIdeologyKinshipNormativeHumanitiesInheritance (genetic algorithm)SociologyPoliticsPhilosophyPolitical scienceLawEpistemologyAnthropology

Abstract

fetched live from OpenAlex

Examining the Antonio plays by John Marston, I argue that the metaphors used to portray familial emotions reveal the ideologies that underpin both excessive and normative versions of familial relationships; these metaphors reveal the pressures placed on family emotions by economic and political ideologies. While critics have traditionally read instances of family breakdown in plays as moments that violate kinship norms, I argue that such moments of violence are caused by ideologies associated with inheritance structures which underpin descriptions and experiences of normative familial emotions.
 A travers l’examen des pièces de théâtre d’Antonio de John Marston, je soutiens que les métaphores employées pour représenter les émotions familiales font apparaître les idéologies qui sous-tendent tant des versions excessives que des modèles normatifs pour lees relations familiales. Ces métaphores révèlent la pression que font subir aux émotions familiales les systèmes de pensée économiques et politiques. Alors que les critiques ont traditionnellement lu les exemples d’éclatement familial dans le théâtre comme des moments violant les normes de la parenté, je soutiens que de tels moments de violence sont causés par des systèmes associés aux structures d’héritage qui sous-tendent les descriptions et les expériences des émotions familiales normatives.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.607

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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
Published2014
Admission routes1
Has abstractyes

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