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Record W3008359826 · doi:10.1080/24692921.2020.1718977

Following Bradshaw and Bishop into<i>Jacob’s Room</i>: British and Canadian editing strategies (tunnelling the textual hotspots, minding the gaps)

2020· article· en· W3008359826 on OpenAlexaboutno aff
Jane Goldman

Bibliographic record

VenueFeminist Modernist Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicPoetry Analysis and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipArt historyHistoryLiteratureArtClassicsLawPolitical science

Abstract

fetched live from OpenAlex

The pioneering editorial practices of Canadian Professor Edward Bishop and the late Professor David Bradshaw offer distinct but complementary approaches to annotating Woolf’s Jacob’s Room (1922). Bishop, editors of the Shakespeare Head edition of the novel and of a transcription of the holograph draft, has gifted new modernist editing the hugely influential, crucial instruction “Mind the gap!,” urging close attention to Woolf’s spacing and lay-out of the material page as important literary signifiers. “Mind the word!” might well be the riposte of Bradshaw (1955–2016) who has left a body of scholarship pressing for close critical attention to every passing cultural, material reference, not least in the numerous proper names of people, places, etc., populating this highly allusive, densely palimpsestic text. Building on Bradshaw’s critical technique and research into the onomastics and Bishop’s editorial insights into the spaces and gaps in Jacob’s Room, this essay explores some of my findings whilst working as an editor and annotator of the work for the forthcoming Cambridge UP edition. “tunnelling,” as Woolf herself called it, into the signifying networks behind her writing, to consider questions of paternity, war and national identity that hang over Jacob’s Room: “‘Did you ever hear who his father was?’”

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)0.006
metaresearch head score (Gemma)0.021
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 score0.119
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0470.037
Scholarly communication0.0210.005
Open science0.0020.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.002

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 head0.052
GPT teacher head0.267
Teacher spread0.215 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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