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Record W7019711455

Il numero dei drammi satireschi sofoclei: Sofocle alle Lenee ed i drammi prosatirici

2012· article· en· W7019711455 on OpenAlexaboutno aff

Bibliographic record

VenueCINECA IRIS Institutial research information system (University of Pisa) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVictoryQuarter (Canadian coin)Set (abstract data type)Tragedy (event)Competition (biology)
DOInot available

Abstract

fetched live from OpenAlex

The aim of this paper is to provide arguments for a lower number of satyr plays in the corpus of Sophocles than usually assumed. Since in classical Athens playwrights used to enter the dramatic competition at the Great Dionysia with a set of three tragedies plus one satyr play, the common belief in modern research is that roughly a quarter of Sophocles’ literary output – amounting to 120 dramatic pieces – must be satyric and not tragic (§ 1). The present paper puts forward two sets of arguments to confute this method of reasoning. First (§ 2), a case is made for the hypothesis that Sophocles staged plays not only at the Great Dionysia, but also at the Lenaia, where only tragedies and no satyr plays at all were performed (a major piece of evidence in support of this hypothesis is IG II2 2319, which may record a victory of Sophocles at the Lenaia in 419-18 BC). Secondly (§ 3), it shows why it cannot be ruled out that Sophocles, like Euripides, also wrote ‘prosatyric’ plays (“Alcestis-like” cases). Since the number of Sophoclean satyr plays is therefore likely to have been lower than traditionally assumed, the paper urges future researchers to greater caution when proposing new identifications of satyr plays in the body of this poet’s fragments (§ 4).

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)0.005
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: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.005
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.004

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 head0.085
GPT teacher head0.339
Teacher spread0.254 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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