Tracking the English novel in <i>Anna Karenina</i>: who wrote the English novel that Anna reads?
Bibliographic record
Abstract
England merits the title of most novelistic country … Nowhere does the novel thrive so readily as in England. Dozens of new novels come out every month. Fatherland Notes , 1866 Anna […] asked Annushka to bring out a little lamp, attached it to the armrest of her seat, and took a paper-knife and an English novel from her handbag. At first she was unable to read […] and [then] Anna began to read and to understand what she was reading […] Anna Arkadyevna read and understood, but it was unpleasant for her to read, that is, to follow the reflection of other people's lives. She wanted too much to live herself. When she would read about the heroine of the novel taking care of a sick man, she wanted to walk with inaudible steps round the sick man's room; when she would read about a Member of Parliament giving a speech, she wanted to give that speech; when she would read about how Lady Mary rode to hounds, taunting her sister-in-law and amazing everyone with her courage, she wanted to do it herself. But there was nothing to do, and so, fingering the smooth knife with her small hands, she forced herself to read. The hero of the novel was already beginning to achieve his English happiness, a baronetcy and an estate, and Anna wished to go with him to this estate, when suddenly she felt that he must be ashamed and that she was ashamed of the same thing. ( Anna Karenina , pt. 1, ch. 29: 99–100; PSS 18: 106–7)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".