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The Main African Studies Think Tank in Russia

2019· article· en· W2996574209 on OpenAlexaboutno aff
I. O. Abramova

Bibliographic record

VenueJournal of the Institute for African Studies · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEconomic, Social, and Public Health Issues in Russia and Globally
Canadian institutionsnot available
Fundersnot available
KeywordsRivalryCompetitor analysisPoliticsPolitical scienceStrategic studiesPower (physics)Middle EastDevelopment economicsEconomic powerEconomyGeographyLawEconomicsManagement

Abstract

fetched live from OpenAlex

The modern world is going through the next stage of its transformation. The old world centers of power – the USA and Canada, the EU, and Japan – are gradually giving away their global economic positions to competitors, especially young rapidly growing economies. In conditions of an extremely dangerous direct confrontation between the “old” and “new” players, the geostrategic and military-political importance of the “periphery” zones of rivalry – the Middle East and Africa has increased. Currently, Africa is developing faster than all other regions of the world and has accumulated quite a lot of potential in recent years. The scientific understanding of these processes and the development of specific recommendations constitute the main activity of the Institute for African Studies of the Russian Academy of Sciences, which marks its 60th anniversary in 2019. Through those years the Institute has accumulated an outstanding scientific potential, which allowed this think-tank to occupy an important place among the African studies research centers of the world.

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)0.012
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.139
GPT teacher head0.430
Teacher spread0.291 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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