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Record W4410484060 · doi:10.1093/isp/ekaf008

High-Level Leader Visits: A Promising Area of Study in IR

2025· article· en· W4410484060 on OpenAlexaff
Ali Balcı, James D. Kim, Jonathan D. Moyer, Collin J. Meisel, Kylie McKee, Alexander Baturo, Byungwon Woo, Seulah Choi, Minseon Ku, Eric Van Rythoven, Marcus Holmes

Bibliographic record

VenueInternational Studies Perspectives · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsCarleton University
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsPsychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Once deemed a “nearly impossible” area of study, high-level leader visits—one of the most visible and consequential practices in global politics—have recently attracted renewed scholarly attention. This resurgence stems from the growing recognition of leader visits as a valuable lens for addressing foundational questions in international relations, including why states act as they do, the consequences of their actions, and how their behavior shapes their global standing. By mapping the current research landscape on leader visits, we highlight its potential for advancing theoretical frameworks and methodological approaches. To further develop this field, we advocate for innovative research methodologies, new avenues of inquiry, and enhanced data collection efforts, paving the way for deeper and more systematic analyses of leader visits in global politics.

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)0.050
metaresearch head score (Gemma)0.042
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: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.009
Science and technology studies0.0050.013
Scholarly communication0.0150.019
Open science0.0030.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0190.002

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 head0.092
GPT teacher head0.432
Teacher spread0.340 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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