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Record W2794886874 · doi:10.4000/res.681

Некрополи террора на территории Санкт-Петербурга и ленинградской области

2015· article· ru· W2794886874 on OpenAlexaff
Alexander D. Margolis

Bibliographic record

VenueRevue des études slaves · 2015
Typearticle
Languageru
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsCanadian Historical Association
Fundersnot available
KeywordsFortress (chess)MemorializationPoliticsPrisonHistoryGrassrootsSpanish Civil WarHumanitiesArtArchaeologyAncient historyLawPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.008

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.

Opus teacher head0.135
GPT teacher head0.347
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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