The Cross-Border Metropolis in a Global Age: A Conceptual Model and Empirical Evidence from the US–Mexico and European Border Regions
Bibliographic record
Abstract
In a globalising urban world, cross-border metropolises are important spatial configurations that reflect the interplay between the space of flows and the space of places. This article scrutinises the different logics at play as urbanisation occurs around international boundaries. It disentangles the contradictory “bordering dynamics” that shape cross-border urban spaces in the context of globalisation and territorial restructuring. Because national borders embody multifaceted as well as ambivalent roles and meanings, they can be viewed as critical barometers for understanding how globalisation impacts cross-border metropolitan space. The first two sections of the article explore the two globalisation processes—“debordering” and “rebordering”—that define the formation of cross-border metropolises. We view the border as a social and political construction; as such, we propose a conceptual framework that addresses the changing role and significance of boundaries in the making of cross-border metropolises. Finally, we offer two contrasting empirical case studies, one from the US–Mexico border, the other from a European border region. By studying bordering dynamics in San Diego–Tijuana and Geneva, we are able to draw some conclusions about the challenges faced by cross-border metropolitan spaces as well as some mechanisms that will govern their future organisation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".