Governance Regimes for Cross-Border Infrastructure: A Comparative Study of Facilities on the Canada–United States Border
Bibliographic record
Abstract
This article examines the governance regimes that have developed around a neglected but significant type of cross-border institution: the organizations that manage and operate the physical infrastructure that links Canada with the United States across a water border. On land border crossings, no shared infrastructure that must be jointly managed and maintained is required. However, along the 3538 km of the southern Canada–northern United States border (55 per cent of the entire border) that is formed by a waterway (lake or river), some jointly managed physical infrastructure is necessary to link the countries. There are a total of 25 vehicular bridges or tunnels that provide a physical connection across this water border, and these include the busiest crossing points along the entire border in terms of both freight (where Detroit/Windsor leads the way) and passenger traffic (which is heaviest at the Niagara crossings). The successful management of these physical infrastructure resources is of great importance to both countries, and many of these cross-border facilities have become potent symbols of cross-national comity. Though arguably the challenges facing border facility operators are generally similar along the Canada–United States water border, a wide variety of models for the management and governance of shared bridges and tunnels have emerged. This article relies on semi-structured interviews with facility managers and executives within seven border crossing governance regimes (accounting for a total of 18 separate border crossing facilities) and focuses primarily on those spanning the Maine–New Brunswick , New York–Ontario, and Michigan-Ontario borders. Through published descriptions of the formal structures and the interviews, we examine the ways in which interests and perspectives from the two sides of the border are accommodated in the diverse governance regimes of these facilities. We also explore the various responses of facility operators to the challenges posed by the binational context; the impact of securitization and new border crossing requirements; and the facility’s role in fostering cross-border relationships at the community level. From this, it is clear that border facilities are generally well operated and responsive to their mission. However, we are able to document some enduring challenges faced by the operators of border infrastructure. These constrain the performance of these facilities in significant ways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".