Can Spatial Proximity to Another Country Drive Short-distance Transnationalism? Evidence from Social Ties in Three European Border Regions
Bibliographic record
Abstract
This article draws inspiration from social network analysis (SNA) to investigate the impact of spatial proximity on the geography of strong social ties in three European border regions. We consider cross-border friendship and kinship as a measure of short-distance transnationalism, examine the conditions under which the latter is likely to emerge, and systematically compares our findings with “traditional” forms of transnationalism. The study is based on a large-scale and representative quantitative survey (N = 3,215) conducted in the Geneva, Lille, and Basque border regions in order to assess the impact of cross-border integration intensity on the inter-personal relationships of all residents, regardless of their migration experiences. The results indicate that only 11.9% of the residents reported having at least one strong cross-border tie. Binomial regression models are used to demonstrate that cross-border ties are not solely shaped by proximity, but also depend on individuals’ socioeconomics, on their mobility experiences, and on contextual factors. This article shows that cross-border integration primarily fosters cross-border friendships rather than kinships, and that cross-border ties are not evenly distributed among the population. Individuals born in the studied border regions do not have the highest levels of short-distance transnationalism, raising a discussion about the importance of promoting cross-border rootedness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".