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Record W4321436458 · doi:10.22215/cjers.v16i1.3783

A “Good” Samaritan? The Geopolitics of Russia’s Covid-19 Assistance

2023· article· en· W4321436458 on OpenAlexvenueno aff
Mariya Y. Omelicheva

Bibliographic record

VenueThe Canadian Journal of European and Russian Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsHumanitarian aidPolitical scienceAuthoritarianismCoronavirus disease 2019 (COVID-19)Government (linguistics)Foreign policyPoliticsState (computer science)PandemicPower (physics)Independence (probability theory)Political economyDevelopment economicsDemocracySociologyLawEconomics

Abstract

fetched live from OpenAlex

Between March and December of 2020, more than three dozen states received various types of COVID-19 assistance from Moscow. The Russian government emphasized a humanitarian character of what has become the largest package of emergency aid since Russia’s independence. The Western governments and commentators cautioned that Moscow had strategic and nefarious motives in choosing the recipients of its coronavirus aid. This study theorizes humanitarian aid allocations by authoritarian states and tests theoretical expectations using novel data on Russia’s COVID-19 aid allocations. Far from being driven by humanitarian concerns, Russia has used humanitarian assistance for projecting power on the global stage and supporting diverse political objectives. Moscow’s use of humanitarian aid for geopolitical benefits has not been a critical disruptor in the humanitarian system by itself. However, jointly with other instruments of foreign policy, Russia’s approaches to humanitarianism can be detrimental to the future of the international humanitarian system.

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)0.002
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: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.091
GPT teacher head0.359
Teacher spread0.268 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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