Bibliographic record
Abstract
This article is a review of regional cross-border coordination and cooperation around the world. Two questions are raised: when trade dominates, does economic or functional interdependency result in cross-border linkages? Second, when politics and institutions mediate cross-border relations, do economic relations intensify? Specifically, do local-central networks of government actors and institutions mediate such processes when they emerge? To investigate those two questions, this work focuses on cross-border relations in various parts of the world primarily focusing of the role trading relations or local-central relations would play in developing cross-border networks spanning an international boundary. In an era of globalisation, increased trade across regions of the world seem to have led to a specific increased cross-border cooperation, however, taking different forms from intense trading relations to resulting cross-border institutionalisation. Those forms of cross-border cooperation in the various regions of the world, however, do not result from the same drivers: For the purpose of a comparative analysis of cross-border relations, the argument developed here is that regional drivers determine types of relations from no relations to intense trading and government-like forms of cooperation. However, in most cases as suggested below, the prime drivers of cross-border relations, trade, do not necessarily translate into increased border spanning governmental activism, and government cross-border institutionalisation does not necessarily transmute into increased economic integration.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".