Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it