Bibliographic record
Abstract
Abstract Stories of a visit to the realm of the dead and a return to the upper world are among the oldest narratives in European literature, beginning with Homer’s Odyssey and extending to contemporary culture. This volume examines a series of fictional works by twentieth- and twenty-first century authors, such Toni Morrison and Elena Ferrante, which deal in various ways with the descent to Hades. Myths of the Underworld in Contemporary Culture surveys a wide range of genres, including novels, short stories, comics, a cinematic adaptation, poetry, and juvenile fiction. It examines not only those texts that feature a literal catabasis, such as Neil Gaiman’s Sandman series, but also those where the descent to the underworld is evoked in more metaphorical ways as a kind of border crossing, for instance Salman Rushdie’s use of the Orpheus myth to signify the trauma of migration. The analyses examine how these retellings relate to earlier versions of the mythical theme, including their ancient precedents by Homer and Vergil, but also to post-classical receptions of underworld narratives by authors such as Dante, Ezra Pound, and Joseph Conrad. Arguing that the underworld has come to connote a cultural archive of narrative tradition, the book offers a series of case studies that examine the adaptation of underworld myths in contemporary culture in relation to the discourses of postmodernism, feminism, and postcolonialism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".