MétaCan
Menu
Back to cohort
Record W2506454281 · doi:10.1057/9781137320117_10

(Un)Editing with (Non-)Fictional Bodies: Pope’s Daggers

2014· book-chapter· en· W2506454281 on OpenAlexaboutno aff
Lynette Hunter, Peter Lichtenfels

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2014
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsMagistrateRomanceComicsArtComedyPresentation (obstetrics)LiteratureDutyTragicomedyVisual artsHistoryLawPolitical science

Abstract

fetched live from OpenAlex

(Un)editing with non-fictional bodies turns to debates around ‘theatricality’ to unmake and remake the playtext and decentre the fixity of the script. 1 The occasion for this attempt or chapter is the recent attention being paid to the two manuscript versions of The Humorous Magistrate which has led us to think through the implications of editing, unediting and (un)editing for a text that has never before been edited, and at the same time, to bring to scholarly editing the impact of a complex and sophisticated understanding of the theatre. The two versions of the play, The Humorous Magistrate , are found in Arbury Hall A414, Warwickshire, and Osborne MsC 132.27, University of Calgary Special Collections. 2 Dating from the early seventeenth-century, the two versions represent distinct stages in the composition of the play: the Arbury manuscript presents a heavily worked over and revised early version of the play, and the Osborne presents a more polished presentation copy and incorporates many of the revisions witnessed in the Arbury (and some other revisions, as well). The play itself is a five-act romantic comedy set in rural England, featuring a corrupt Justice Thrifty, his daughter and her suitor, and several other comic characters who explore the themes of love, loyalty, and familial and marital duty.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.016
Scholarly communication0.0120.006
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.007

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 head0.028
GPT teacher head0.203
Teacher spread0.175 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooksSame topicDigital Humanities and ScholarshipFrench-language works237,207