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Record W4255161906 · doi:10.3138/utq.80.1.078

Orwell on Jura: Locating <i>Nineteen Eighty-Four</i>

2011· article· en· W4255161906 on OpenAlexvenueno aff
Paul Delany

Bibliographic record

VenueUniversity of Toronto Quarterly · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsnot available
Fundersnot available
KeywordsTrial by ordealGeorge (robot)HistoryWorld War IISpanish Civil WarArtLiteratureArchaeologyArt historyPhilosophyTheology

Abstract

fetched live from OpenAlex

George Orwell's biographers have been divided about his move to the island of Jura in the last years of his life. Some have seen it as a refuge from the trials of London life during the war; others as a bleak and inaccessible place, chosen in one of Orwell's masochistic gestures. The ordeal of writing Nineteen Eighty-Four on Jura has been described as a suicidal project. But Orwell wanted to be ‘a farmer who wrote’ after the war, for both sentimental and practical reasons. Life on Jura was in some ways healthier and more comfortable than London, and Orwell certainly was happier there than in most other places he lived. If he had survived, he probably would have continued to live in the countryside – still producing his books, but also cultivating his vegetables.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.159
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0070.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0330.006

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 head0.025
GPT teacher head0.223
Teacher spread0.197 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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