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Record W2413407275 · doi:10.1017/cbo9781107239081

George Bernard Shaw in Context

2015· book· en· W2413407275 on OpenAlexaff
Brad Kent, Peter Gahan, Lauren Arrington, Peter Conolly-Smith, Desmond Harding, Éibhear Walshe, Nicholas Grene, Anthony Roche, Margot S. Peters, Sos Eltis, J. Ellen Gainor, Kerry Powell, Ellen Dolgin, Heidi J. Holder, Jean Chothia, John McInerney, Elizabeth Carolyn Miller, Charles A. Carpenter, Lawrence Switzky

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldMedicine
TopicLiterature Analysis and Criticism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanityGeorge (robot)PoliticsContext (archaeology)Formative assessmentPersonalitySociologyPerformance artAestheticsSocial scienceArt historyEnvironmental ethicsArtHistoryPsychoanalysisPsychologyPolitical sciencePhilosophyLawPedagogyArchaeology

Abstract

fetched live from OpenAlex

When Shaw died in 1950, the world lost one of its most well-known authors, a revolutionary who was as renowned for his personality as he was for his humour, humanity, and rebellious thinking. He remains a compelling figure who deserves attention not only for how influential he was in his time, but for how relevant he is to ours. This collection sets Shaw's life and achievements in context, with 42 scholarly essays devoted to subjects that interested him and defined his work. Contributors explore a wide range of themes, moving from factors that were formative in Shaw's life, to the artistic work that made him most famous and the institutions with which he worked, to the political and social issues that consumed much of his attention, and, finally, to his influence and reception. Presenting fresh material and arguments, this collection will point to new directions of research for future scholars.

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)0.002
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.011
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.003

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 head0.020
GPT teacher head0.225
Teacher spread0.205 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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