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Record W2811238962 · doi:10.3138/ecf.30.4.473

The Virgin and the Spy: Authority, Legacy, and the Reading Public in Eliza Haywood’s <i>The Invisible Spy</i>

2018· article· en· W2811238962 on OpenAlexvenueno aff
Daniel Froid

Bibliographic record

VenueEighteenth-Century Fiction · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCynicismReading (process)The ImaginaryLiteratureArtAestheticsMedia studiesSociologyLawPolitical sciencePoliticsPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

This essay addresses the self-referential tendencies of Eliza Haywood’s The Invisible Spy (1754), as well as its reflections on authorship and print culture and its relationship to Haywood’s legacy. The Invisible Spy, like other works in Haywood’s oeuvre, simultaneously deploys and interrogates literary techniques commonly associated with low culture, including amatory fiction. In the figure of Explorabilis, the Invisible Spy, Haywood creates a proxy that allows her to comment on the insincerity and cynicism of the eighteenth-century print marketplace. Explorabilis imitates features of the periodical eidolon who expresses concern for contemporary culture, as he claims to provide readers with a text whose edifying qualities justify its exploitative content. The Invisible Spy represents both a commentary on mid-eighteenth-century literary culture and an assessment of Haywood’s career: it not only emphasizes her prowess in diverse genres but also suggests a sense of cynicism about texts’ manipulation of their readers. In self-consciously enacting the project of reforming imaginary readers, The Invisible Spy demonstrates that such contemporary efforts exploit the same material as the texts they criticize. This essay argues that The Invisible Spy serves as Haywood’s definitive statement about the literary marketplace and her mastery of it.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.207
Teacher spread0.194 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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