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Record W2185524174

Henry Fielding's Whores

2015· dissertation· en· W2185524174 on OpenAlexfundno aff
Kalin Smith

Bibliographic record

VenueMacSphere (McMaster University) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
FundersMcMaster University
KeywordsArt historyArt
DOInot available

Abstract

fetched live from OpenAlex

The mercenary whore is a recurring character-type in Henry Fielding’s plays and early fictions. This thesis examines Fielding’s representations of the sex-worker in relation to popular eighteenth-century discourses surrounding prostitution reform and the so-called ‘woman question’. Fielding routinely confronted, and at times affronted his audience’s sensibilities toward sexuality, and London’s infamous sex-trade was a particularly contentious issue among the moralists, politicians, and religious zealots of his day. As a writer of stage comedy and satirical fiction, Fielding attempted to laugh his audience into a reformed sensibility toward whoredom. He complicates common perceptions of the whore as a diseased, licentious, and irredeemable social other by exposing the folly, fallibility, and ultimate humanity of the modern sex-worker. By investigating three of Fielding’s stage comedies—"The Covent-Garden Tragedy" (1732), "The Modern Husband" (1734), and "Miss Lucy in Town" (1742)—and two of his early prose satires—"Shamela" (1741) and "Joseph Andrews" (1742)—in relation to broader sociocultural concerns and anxieties surrounding prostitution in eighteenth-century Britain, this thesis locates Fielding’s early humanitarian efforts to engender a reformed paradigm of charitable sympathy for fallen women later championed in his work as a justice and magistrate.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.006
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.029
GPT teacher head0.264
Teacher spread0.235 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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