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Record W3094327561 · doi:10.1080/08865655.2020.1833230

A Theme-based Analysis of the Intensity of Cross-Border Cooperation Across Europe

2020· article· en· W3094327561 on OpenAlexvenueno aff
Kadir Basboga

Bibliographic record

VenueJournal of Borderlands Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCross-border cooperationTheme (computing)Political scienceEconomic geographyRegional scienceInternational tradeEconomicsSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

In this paper, focusing on the European Territorial Cooperation (INTERREG) example, the author compares three selected policy themes to understand what drives cross-border cooperation (CBC) in different contexts. The main argument is that cultural, economic, political, institutional, and geographical drivers of CBC play different roles in determining the intensity of CBC across different themes. To test this argument, three separate regressions are run for the policy themes of education, small and medium sized enterprises, and infrastructure. The regression results support that the intensity of CBC in each particular thematic area is driven by a unique combination of factors. This finding provides important insights on CBC policy-making in the European Union and potentially beyond. Follow-up studies can inform the design of future CBC policies and prioritization of thematic CBC interventions.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
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 head0.054
GPT teacher head0.443
Teacher spread0.389 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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