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Record W2980299191 · doi:10.1163/2451-8921-00403005

The Politics of Retirement Age Increase in Russia: Proposals, Protests and Concessions

2019· article· en· W2980299191 on OpenAlexaff
Elena Maltseva

Bibliographic record

VenueRussian Politics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPensionGeopoliticsPension systemPoliticsElement (criminal law)Government (linguistics)Political sciencePolitical economyRussian federationEconomic policyEconomicsLaw

Abstract

fetched live from OpenAlex

The article discusses the changes to the Russian pension system since 2013, focusing specifically on the most recent policy moves. It argues that, despite the apparent instability of the Russian pension system caused by numerous policy shifts that have occurred since 2015, one element has remained constant: since the early 1990s the transformation of the Russian pension system has been driven primarily by neoliberal economic advisers to the Russian government. Passage of the long-delayed decision to raise the retirement age, which provoked large-scale protests, can be understood in light of the current geopolitical and economic risks that complicate the future of Russian economy.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.299
Teacher spread0.284 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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