Factors Influencing Cross-Border Cooperation in North America
Bibliographic record
Abstract
Cross border cooperation (CBC) and integration can contribute to the socio-economic development of states since they allow for international collaboration through the removal of some of the restrictions or barriers that arise from the existence of national borders. Recent academic studies on borders explain that borders have become principal zones of state transformation central to the social and economic growth and development of states and their local and regional communities. It is therefore necessary to understand and examine the factors that contribute to and shape cross-border relations and interactions. These factors can either support or negatively influence cross- border activities and cooperation levels. This, consequentially, impacts the socio-economic growth and development of cross-border regions and their respective states.\nThis paper examines five factors that shape and influence CBC in North America: border types, political institutions, educational institutions, border security, and social capital and inclusion. The paper studies the importance of these five factors to cross-border relations and how they influence the cooperation of North American cross-border regions. By analyzing and comparing the presence and similarities of these factors, the paper highlights the degree to which they impact the creation and functioning of cross-border interactions in two Canada-United States border regions. This will not only aid in showing how important the five forces are in shaping cooperation between border regions but will also explain why certain cross-border regions experience higher CBC levels compared to others.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".