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Record W2470488393 · doi:10.3138/md.59.2.2

Spots of Future Time: Tableaux, Masculinity, and the Enactment of Aging

2016· article· en· W2470488393 on OpenAlexvenueno aff
Andrea Charise

Bibliographic record

VenueModern Drama · 2016
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersSvenska Forskningsrådet Formas
KeywordsNarrativeMasculinityContext (archaeology)DramaAestheticsAppealSubject (documents)Reading (process)ArtSociologyVisual artsLiteratureGender studiesHistoryLinguisticsPhilosophyComputer science

Abstract

fetched live from OpenAlex

This article proposes the utility of tableau as a terminological and conceptual contribution to age studies. I explore how the transgeneric appeal of this theatrical form enables stillness and fragmentation to be a part of the vocabulary of aging. Referring to the work of two twenty-first-century artists – French photocollagist Gilbert Garcin and American novelist David Markson – I demonstrate the applicability of the aesthetics of tableaux to non-theatrical enactments of aging. Despite their generic and formal differences, reading Garcin and Markson in tandem prepares readers to think of older age, and older masculinities especially, outside the framework of narrative and, instead, in terms of the visual aesthetics of stillness. Drawing new links among aging, play, and the tableau form, the article asserts the potential of the static as a way of imagining the aging subject in the context of age studies more generally. The tableau’s aesthetics of living stillness, I conclude, opens up drama and age studies alike to more playful possibilities for imagining aging and for enactments of self-creation in later life.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.576

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.312
Teacher spread0.293 · 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 designOther design
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

Citations3
Published2016
Admission routes1
Has abstractyes

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