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Record W4220823893 · doi:10.22148/001c.33994

Post-War British Women Writers and their Cultural Impact: A Quantitative Approach

2022· article· en· W4220823893 on OpenAlexvenueno aff
Ingo Berensmeyer, Sonja Trurnit

Bibliographic record

VenueJournal of Cultural Analytics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)Period (music)HistorySelection (genetic algorithm)Gender studiesFirst world warBritish literatureSociologyLiteraturePolitical scienceArtAestheticsEnglish literatureLawPoliticsAncient historyComputer science

Abstract

fetched live from OpenAlex

This article uses a quantitative approach to study the reception of women writers in post-war Britain. Using data from two influential journals in the period (1946–1960), the TLS and the Listener, we first establish a list of those contemporary British women writers who were most frequently mentioned in these magazines. We then compare their representation in the magazines to that of three comparison groups: a selection of British male contemporary writers, well-known earlier British women writers, and canonical male authors. We explore how the differential categories of gender and canonicity intersect in the (under ) representation of contemporary women writers, and how this underrepresentation not only holds true for the mid-twentieth century but, at least as it is reflected in the attention paid to writers by TLS reviewers, continues in the later 20th and early 21st century.

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)0.009
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0140.012
Science and technology studies0.0040.005
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.048
GPT teacher head0.343
Teacher spread0.295 · 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.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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