Некрополи террора на территории Санкт-Петербурга и ленинградской области
Bibliographic record
Abstract
In the late 1980s, work began to identify, study, and memorialize the mass burial sites of the victims of Soviet terror. According to official data alone, between 1918 and 1953 in Petrograd/Leningrad 58,000 people were executed for political reasons. A few sites of execution and burial have been identified so far, among which the Peter and Paul Fortress, Levashovo, and the Kovalevsky forest. This article is devoted to the history and analysis of these sites and their memorialization. In Levashovo more than 19,500 people were secretly buried, who had been executed or died in prison between 1937 and 1954. In 1989, city authorities declared this mass grave created by the NKVD the Levashovo Memorial Cemetery of the Victims of Political Repression. Since then, with the help of public organizations, more than 20 national or confessional monuments have been installed, as well as about 1000 personal memorial signs. In 2001, the society Memorial discovered the mass graves of the civil war in the Kovalevsky forest and placed a memorial sign ‘To the Victims of Red Terror’. In the Peter and Paul fortress, systematic archaeological study of the execution site near the Golovkin bastion has been under way since 2010 and has already exhumed the remains of 160 victims executed in the first years of the Soviet regime. But there is yet no agreement on the shape of a future memorial near the walls of the fortress.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".