Bibliographic record
Abstract
In June 1887, while a guest at Tolstoy’s estate, Iasnaia Poliana, the eminent jurist Anatolii Koni told Tolstoy a remarkable story from his own practice. In the early 1870s, while Koni was serving as prosecutor for the St. Petersburg district court, a well-dressed young man “with a pale, expressive face and restless, burning eyes” had come to his office. He asked Koni to overrule a prison official who had refused to transmit without first reading it a letter to a female prisoner named Rozalia Oni. Rozalia Oni was a prostitute of Finnish origin. Convicted of having robbed a client of 100 roubles, she had been sentenced to four months' confinement. Without revealing his motives, the young man said that he wanted to marry the woman. The young man, Koni knew, belonged to a well-known family, was well educated, and held a responsible post in the civil service. Koni tried to dissuade him, saying that Rozalia could never be happy with him, but it was to no avail. Rozalia herself had eagerly agreed to the marriage. Koni refused to expedite the wedding, however, and the advent of Lent necessitated further postponement. During this waiting period Rozalia caught the typhus endemic in Russian prisons and died. As Koni sententiously put it, “The Lord drew a curtain over her life and stopped the beating of her poor heart.”
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 | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".