Bibliographic record
Abstract
The third quarter of 'War and Peace' is where the rubber really hits the road (or the cannonballs hit the walls). The story centres on one of history's most famous periods, Napoleon's March on Moscow. The leading characters are involved in plotting Russia's tactics, fighting on the front or guarding their estates against the expected overrunning by French soldiers. Tolstoy evocatively describes the futile slaughter of war, in some of literature's most dramatic chapters ever written. He also brings Napoleon to life as he arrows in on the famous general. At the end of the Battle of Borodino, two of the main characters are missing, presumed dead. It is a real cliffhanger for 'War and Peace IV'. Leo Tolstoy's masterpiece is a complete semester of Russian and French history, using the zoom button to focus on its impact on families from the aristocracy to the peasants. It paints a picture of petty jealousy, pride and forbidden love in the Russian stately homes. If you like costume dramas and the novels of Jane Austen ('Pride and Prejudice', 'Sense and Sensibility'), this is the granddaddy of them all. The same goes for fans of Bernard Cornwell's 'Sharpe' novels and TV series', starring Sean Bean.'War and Peace' was made into a BBC TV series in 2016, written by Andrew Davies and starring Lily James and James Norton.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".