Bibliographic record
Abstract
Highlights of the extraordinary wartime diaries of Ivan Maisky, Soviet ambassador to London The terror and purges of Stalin’s Russia in the 1930s discouraged Soviet officials from leaving documentary records let alone keeping personal diaries. A remarkable exception is the unique diary assiduously kept by Ivan Maisky, the Soviet ambassador to London between 1932 and 1943. This selection from Maisky's diary, never before published in English, grippingly documents Britain’s drift to war during the 1930s, appeasement in the Munich era, negotiations leading to the signature of the Ribbentrop–Molotov Pact, Churchill’s rise to power, the German invasion of Russia, and the intense debate over the opening of the second front. Maisky was distinguished by his great sociability and access to the key players in British public life. Among his range of regular contacts were politicians (including Churchill, Chamberlain, Eden, and Halifax), press barons (Beaverbrook), ambassadors (Joseph Kennedy), intellectuals (Keynes, Sidney and Beatrice Webb), writers (George Bernard Shaw, H. G. Wells), and indeed royalty. His diary further reveals the role personal rivalries within the Kremlin played in the formulation of Soviet policy at the time. Scrupulously edited and checked against a vast range of Russian and Western archival evidence, this extraordinary narrative diary offers a fascinating revision of the events surrounding the Second World War.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.020 |
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".