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Record W1957984782 · doi:10.1080/1060586x.2013.816104

Russia, the death penalty, and Europe: the ambiguities of influence

2013· article· en· W1957984782 on OpenAlexaff
Matthew Light, Nikolai Kovalev

Bibliographic record

VenuePost-Soviet Affairs · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsWilfrid Laurier UniversityUniversity of Toronto
Fundersnot available
KeywordsHistoryPolitical science

Abstract

fetched live from OpenAlex

Studies of capital punishment worldwide investigate how international influence affects the death penalty. We analyze European influence on the death penalty in Russia over the imperial, Soviet, and post-Soviet periods, using two parameters: the changing mechanisms of influence in each period and the death penalty's significance in the broader spectrum of punitive violence. On the first parameter, in the tsarist period, European influence on Russian policy was “productive” – exercised through prestige, moral suasion, and “diffusion.” In the Soviet period, European influence was blocked. In the post-Soviet period, European influence is coercive, as the Council of Europe has unsuccessfully sought to compel Russia to abolish its death penalty. On the second parameter, the death penalty in Russia has always been only one of many forms of state-sanctioned punitive killing. In consequence, the Council's involvement in Russia's death penalty has produced an incoherent policy outcome and has entangled the Council in Russia's authoritarian politics. Russia thus exemplifies the hazards of external involvement in death penalty abolition.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.020
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2013
Admission routes1
Has abstractyes

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