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Record W2984501462 · doi:10.1080/08865655.2019.1685402

Mapping International Co-authorship Networks in Border Studies (1986–2018)

2019· article· en· W2984501462 on OpenAlexvenueno aff
Olivier Walther, Martin Klatt, Freerk Boedeltje

Bibliographic record

VenueJournal of Borderlands Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsFragmentation (computing)Regional scienceEconomic geographyField (mathematics)Political sciencePublishingPreferenceSocial network analysisSociologyGeographyLawComputer scienceEconomicsSocial capital

Abstract

fetched live from OpenAlex

Border studies have become increasingly global over the past two decades. Yet, a network analysis of the articles published in the Journal of Borderlands Studies from 1986 to 2018 shows that less than half of them have one or more coauthors. Unlike in other scientific disciplines, where a growth of co-publications is observed, this proportion has not really changed over the last decade. Our paper also shows that major divisions can be found within border studies, which is no small paradox for a science supposedly cross-border by nature. Despite the overall global increase in scientific connectivity, internationally co-authored papers are still an exception in our field and scholars have a strong preference for publishing within their own country. Instead of a fully integrated community, they form a fragmented network whose main components are mainly located in the United States. Interviews with border experts reveal that various obstacles contribute to the current fragmentation of border studies. In addition to being separated by geographical distance and, sometimes, by actual walls, border scholars must also able to overcome the cognitive, social, organizational, and institutional distance that separate them.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.439
Teacher spread0.372 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2019
Admission routes1
Has abstractyes

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