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Record W4404496007 · doi:10.29173/spectrum264

The Siren's Song

2024· article· en· W4404496007 on OpenAlexvenueno aff
Aaron Gorner

Bibliographic record

VenueSpectrum · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Archaeological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSiren (mythology)AeronauticsArtEngineeringLiterature

Abstract

fetched live from OpenAlex

Many have been fascinated by Dante’s treatment of Virgil in his Commedia. He is simultaneously Dante’s beloved master, and a character who does not escape Hell. Robert Hollander famously asserts that Dante wields Virgil to classify him as a failed poet-vates, and therefore by contrast, to show himself, Dante, as theologus-poeta. In this paper, I will show that more than demonstrating himself a true prophet, Dante also utilises Virgil to suspend Christian comedy above classical tragedy. This paper will explore the Siren theme throughout Purgatorio, namely in Cantos II, XIX, and XXX, for observing how Dante himself moves beyond the Siren, and concurrently evinces Virgil’s failure to do so. As Beatrice is contrasted to the Siren, Dante is paired with Virgil, his Commedia with the Aeneid. In making this argument, I tie everything together by showing how the appearance of Beatrice alludes to Nisus and Euryalus (a hitherto unnoticed allusion), the very characters that Virgil had written to insert higher morality into Homer’s Odysseus and Diomedes. While those characters met tragic end, Dante and Beatrice, by contrast, are reunited in Christian splendor—that is, redemptive and transformative grace. Dante’s Commedia is therefore a comedy because Dante moves beyond the Siren to Beatrice, a feat that Virgil was not able to accomplish.

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)0.002
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 score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0440.006

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 head0.021
GPT teacher head0.205
Teacher spread0.184 · 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
Published2024
Admission routes1
Has abstractyes

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