Bibliographic record
Abstract
In her diary entry for March 3, 1877, when the novel Anna Karenina was almost finished, Tolstoy’s wife Sofya recorded her husband’s words: “In order for a work to be good, one must love its main basic idea, as in Anna Karenina I love the idea of family [ mysl' semeinuiu ].” How does this idea agree with what is generally thought to be the main theme of the novel - passionate love that transgresses all boundaries? Does Tolstoy not love Anna, this beautiful woman, so charmingly depicted when we (and Vronsky) first meet her? woman, so charmingly depicted when we (and Vronsky) first meet her? Her shining gray eyes, that looked dark from under the thick lashes, rested with friendly attention on his face, as though she were recognizing him … In that brief look Vronsky had time to notice the suppressed eagerness which played over her face, and flitted between the brilliant eyes and the faint smile that curved her red lips. It was as though her nature were brimming over with something that against her will showed itself now in the flash of her eyes, and now in her smile. (I, 18; 73; PSS 18: 66)
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".