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Record W4401910017 · doi:10.1002/leap.1619

Regional spillover effect of 2022 sanctions against <scp>Russia</scp> on scholarly publications

2024· article· en· W4401910017 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLearned Publishing · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSpillover effectSanctionsBusinessInternational tradePolitical scienceEconomicsLawMicroeconomics

Abstract

fetched live from OpenAlex

Abstract This article explores the spillover effects of economic sanctions against Russia on research in neighbouring countries. The assumption of the paper is that such effects should take place given the high level of regional integration in the post‐Soviet area. The study uses bibliometric data retrieved from the Web of Science for analysis; more specifically, the data on publications during 2019, 2021 and 2023 from each of the four countries of interest – Kazakhstan, Belarus, Ukraine and Russia. The data was analysed using descriptive statistics and graphs. The results clearly point to the potential presence of negative externalities of economic sanctions on research systems of neighbouring countries not directly involved in the war. The paper discusses implications of the effects and recommendations, which can be used by policy makers to alleviate the effects on the neighbouring countries and by scholars to further investigate the phenomenon.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.007
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.045
GPT teacher head0.257
Teacher spread0.212 · 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