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Record W1974216877 · doi:10.1093/notesj/gjt184

The Source for Hume's Anecdote about Sophocles in his Letter to Anne-Robert-Jacques Turgot

2013· article· en· W1974216877 on OpenAlexaff
James C. Miller

Bibliographic record

VenueNotes and Queries · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAnecdoteVerdictPhilosophyJuryOrder (exchange)LiteratureTragedy (event)ClichéArtLawPolitical science

Abstract

fetched live from OpenAlex

HUME, in a letter to Anne-Robert-Jacques Turgot, relates the following about Sophocles: We are told that Sophocles in his old Age was supposed to have lost his Senses; and his Family for that Reason apply’d for a Commission of Lunacy against him. But the Poet, as his only Answer, read some Scenes of his Tragedy of Œdipus Colonnus which he was at that time composing. And he was unanimously acquitted by the Judges.1 J. Y. T. Greig, the editor of The Letters of David Hume, does not tell us from where Hume is getting this anecdote, but an account of it can be found in Cicero’s De Senectute: Sophocles composed tragedies to extreme old age; and when, because of his absorption in literary work, he was thought to be neglecting his business affairs, his sons haled him into court in order to secure a verdict removing him from the control of his property on the ground of imbecility. … Thereupon, it is said, the old man read to the jury his play, Oedipus at Colonus, which he had just written and was revising, and inquired: ‘Does that poem seem to you to be the work of an imbecile?’ When he had finished he was acquitted by the verdict of the jury.2

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)0.013
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0120.007
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0090.003

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 head0.014
GPT teacher head0.261
Teacher spread0.247 · 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
Published2013
Admission routes1
Has abstractyes

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