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Record W1989956291 · doi:10.3138/ecf.26.2.233

Anachronistic Aesthetics: Maria Edgeworth and the “Uses” of History

2013· article· en· W1989956291 on OpenAlexvenueno aff
Mary L. Mullen

Bibliographic record

VenueEighteenth-Century Fiction · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnachronismIrishContingencyPoliticsRomanceAestheticsContext (archaeology)EnlightenmentHistoryOrder (exchange)NationalismLiteratureSociologyArtPhilosophyEpistemologyLawLinguisticsArchaeologyPolitical science

Abstract

fetched live from OpenAlex

Scholars often understand Maria Edgeworth as a belated Enlightenment writer in a Romantic age because she seeks to organize both her fiction and the history it represents so that they can be put to use. In this article, however, I argue that Maria Edgeworth’s Irish writing legitimates lived relationships between past and present that her politics wished to eradicate. Although she attempts to periodize within her fiction to shape a useful history—separating past and present in order to bring about an imagined future—her anachronistic aesthetics unsettle her historical periods and show the political value of discordance, contingency, and historical misuse. Focusing especially on An Essay on Irish Bulls and Castle Rackrent, I consider how Edgeworth’s anachronisms imagine political possibilities that do not simply support either union with England or Irish nationalism. The heterogeneity created by portable aesthetic forms—whether literary language that transcends its historical context or forms of metalepsis that propel readers forward and backward in time—foster transhistorical relationships that expand our understanding of the present and imagine a more open future.

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)0.003
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.039
Scholarly communication0.0100.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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Same venueEighteenth-Century FictionSame topicIrish and British StudiesFrench-language works237,207