Bibliographic record
Abstract
In order to sharpen her understanding of how narrative distance from character could be achieved in fiction, Elizabeth Bowen turned to French novelists, especially Gustave Flaubert, Henri de Montherlant, Guy de Maupassant, and Marcel Proust. She found in French novels examples of narratorial cruelty towards characters. She also adopted the Proustian idea that literature is always a translation of sorts, whether from one language to another or from reality to representation. As previously unexamined archival material proves, Bowen turned her hand to translating passages from Flaubert's L'Éducation sentimentale and Proust's À la recherche du temps perdu in the early 1930s. She also made an attempt to index Flaubert's correspondence. Throughout her career, Bowen commented frequently on French fiction. She reviewed Henri de Montherlant's Pitié pour les femmes and Les jeunes filles when those volumes appeared in an English translation in 1937. She wrote prefaces to Flaubert's major works. In part, she admired the way that national differences were inscribed in French and English fiction. But she principally looked to French fiction for examples of the grandiosity – or littleness – of character within historical frameworks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".