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Record W2900389384 · doi:10.3103/s0147688218030103

Some Aspects of the Development of the All-Russian Institute for Scientific and Technical Information

2018· article· en· W2900389384 on OpenAlexaboutno aff
Yu. N. Schuko

Bibliographic record

VenueScientific and Technical Information Processing · 2018
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Philosophical Inquiry
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringQuarter (Canadian coin)Political scienceLibrary scienceRegional scienceGeographyComputer scienceLawArchaeology

Abstract

fetched live from OpenAlex

Although the USSR and the United States had a comparable number of employees in the field of scientific information, there was a substantial difference in funding, which resulted in a lack of information support for Soviet scientists. While 90% of the publications in the United States were available almost immediately after their release, in the USSR they were delayed by 1.5 to 2 years. Since the publication of the Abstracts Journal by VINITI (AJ), its content was consistently expanding and reached its peak in 1990 (1.5 million documents per year). The next quarter of a century was characterized by a decline in both the composition of the AJ and the time required for its document coverage. The steps for restructuring VINITI activities are discussed. The focus is made on improving the limited coverage of the Russian-language part of the global flow of scientific information by Western information systems.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0130.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.060
GPT teacher head0.317
Teacher spread0.257 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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