A Synthesis on Cross-Border Travel: Focus on El Paso, Texas
Bibliographic record
Abstract
Because of the importance of cross-border transactions to local, regional, and national economies, a complete understanding on the characteristics of cross-border travel is invaluable. In addition, for all modes of cross-border travel, improved efficiency is often preferred as an overarching solution, and yet formulating specific improvement strategies depends upon a fundamental understanding of the cross-border issues as well as the factors influencing the travel. The objective of this study is thus to provide a synthesis review to examine cross-border travel, explain influences on that travel, and contribute toward a better understanding the travel behavior decisions crossing an international border bridge. The current study has a particular focus on the case of El Paso, Texas; however, the study also provides valuable insights in the context of other U.S. border areas, both Canadian and Mexican, demonstrating the border’s import to the U.S. This study can serve as the basis for a comprehensive analysis and a robust model to understand the cross-border travel choice decisions. Moreover, as it provides a more complete understanding, this focused review contributes substantially to the local agencies’ analysis toolbox when considering proposed policy changes, capital improvements, and techniques to market cross-border travel to particular population groups, such as pedestrians.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".