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Record W4247252812 · doi:10.1515/9781501753497

Irregular Unions

2021· book· en· W4247252812 on OpenAlexfundno aff
Katharine Cleland

Bibliographic record

VenueCornell University Press eBooks · 2021
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsnot available
FundersUniversity of TorontoHarvard UniversityUniversity of OxfordUniversity of CambridgeUniversity of PennsylvaniaFolger InstituteFolger Shakespeare Library
KeywordsPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Katharine Cleland's Irregular Unions provides the first sustained literary history of clandestine marriage in early modern England and reveals its controversial nature in the wake of the Elizabethan Religious Settlement, which standardized the marriage ritual for the first time. Cleland examines many examples of clandestine marriage across genres. Discussing such classic works as The Faerie Queene , Othello , and Merchant of Venice , she argues that early modern authors use clandestine marriage to explore the intersection between the self and the marriage ritual in post-Reformation England. The ways in which authors grapple with the political and social complexities of clandestine marriage, she finds, suggest that these narratives were far more than interesting plot devices or scandalous stories ripped from the headlines. Instead, after the Reformation, fictions of clandestine marriage allowed early modern authors to explore topics of identity formation in new and different ways.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.010

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 head0.036
GPT teacher head0.160
Teacher spread0.124 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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Same venueCornell University Press eBooksSame topicCorporate Governance and LawFrench-language works237,207