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The Making of British Bourgeois Tragedy

2019· book· en· W4231440328 on OpenAlexaff
Alex Eric Hernandez

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBourgeoisieTragedy (event)ArtHistoryAestheticsLiteraturePolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Abstract This book assembles a body of print and performance concerned with the misfortunes of the middling sort, arguing that these works negotiated tragedy’s vexed relationship to ordinary life. This “bourgeois and domestic tragedy” imagined a particularly modern sort of affliction, an “ordinary suffering” divested of the sorts of meanings, rhetorics, and affective resonances once deployed to understand it. Whereas neoclassical aesthetics aligned tragedy with the heroic and the admirable, bourgeois tragedy treated the pain of common people with dignity and seriousness, meditating upon a suffering that was homely, familiar, realistic, and entangled in the nascent values of capitalism, yet no less haunted by God. Hence, where many have seen aesthetic stagnation, misfiring emotion, and the absence of an idealized tragicness in the genre, this book tracks instead a sustained engagement in the emotional processes and representational techniques through which the middle rank feels its way into modernity. Describing this shift as an episode in the histories of both tragedy and emotion, it revises the standard critical account of eighteenth-century tragedy and reads the genre’s emergence in the period as a vigorous cultural conversation over whose life—and whose way of life—is grievable, as well as how that mourning might be performed.

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)0.002
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.025
Scholarly communication0.0100.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.057
GPT teacher head0.249
Teacher spread0.192 · 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
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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