Borders, border regions and economic integration: One world, ready or not
Bibliographic record
Abstract
The effects of a border on economic interaction depend on the nature of that border with respect to the degree of openness, the degree of cultural, racial and linguistic differences, political relations between the respective regions and the degree of economic disparity. High walls and slow border crossings are detrimental to economic exchange. Economic and political tensions surrounding a border are directly related to the degree of economic disparity. At the same time, large differentials in relative factor costs (i.e. cheaper capital on one side and cheaper unskilled labor on the other) tend to encourage cross-border production sharing, as well as cross-border shopping and crossborder working. The extent and shape of border relationships vary widely and are strongly influenced by the degree of asymmetry in the neighboring economies, as well as the social and political organization of each. This paper presents a discussion of factors affecting the degree and nature of economic interactions between borders that are moving toward increased economic integration. It addresses the question of why in some cases with all political barriers removed, barriers to trade and cooperation persist, while in other cases large amounts of trade and cooperation exist despite substantial political barriers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".