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Record W4212774987 · doi:10.3406/russe.2015.2688

Les années terribles - Лихие годы

2015· article· en· W4212774987 on OpenAlexaboutno aff
Véronique Jobert

Bibliographic record

VenueRevue Russe · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSecurity, Politics, and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)HistoryEconomic historyHumanitiesPolitical scienceGeographyArtArchaeology

Abstract

fetched live from OpenAlex

Back to the 1990s. The ‘dire years’, as the 1990s are generally called in Russia , deserve to be better designated. A quarter of a century later , this autumn in Moscow, two events took place which provided this opportunity : Yury Afanasyev’s death in September and, a few days later, a festival organized in the Muzeon park. As a matter of fact , during those years, Russia benefitted from a degree of freedom of speech never known before in the country. This occurred thanks to a new law regarding freedom of the press, published in August 1990. One should never forget that.

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)0.001
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0360.011

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 head0.222
GPT teacher head0.386
Teacher spread0.164 · 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
GenreOther

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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