Bibliographic record
Abstract
From Edna O’Brien’s earliest novels, many commentators have noted that desire is at the core of the narratives. The true irony in such observations lies in their frequently blinkered understanding of what comprises that desire, reducing it to a heteronormative, Barbara-Cartland-style pursuit of “romance.” While the characters themselves may think this is what they hunger for, the text inevitably opens up vaster sources of insatiable longing. As Mary Douglas has established, “the body is capable of furnishing a natural system of symbols” (xxxii), and in O’Brien’s texts the human mouth, especially when at its most “animal,” metonymizes numerous desires, most often balked and even impossible ones, including those that actuate the scene of writing. Mouths are everywhere in O’Brien’s novels, licking, yawning, weeping, swallowing, keening, grimacing, biting, shrieking, chewing, singing, speaking, and opening in silence. These mouths give voice to the immaterial, and even animate the inorganic, which, for all of its immateriality, can yet resist manipulation. The inscrutable “inhuman” voice that emerges ultimately reveals “that words themselves are sphinxes, hybrids of the animal, the human, and the inorganic” (Ellmann 77).
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".