Bibliographic record
Abstract
Canada and the United States exchange the world's highest level of bilateral trade, valued at $1.4 billion a day. Two-thirds of this trade travels on trucks. Heavy Traffic examines the way in which the regulatory reform of American and Canadian trucking, coupled with free trade, has internationalized this vital industry. Before deregulation, restrictive entry rules had fostered two separate national highway transportation markets, and most international traffic had to be exchanged at the border. When the United States deregulated first, the imbalance between its opened market and Canada’s still-restricted one produced a surprisingly difficult bilateral dispute. American deregulation was motivated by domestic incentives, but the subsequent Canadian deregulation blended domestic incentives with transborder rate comparisons and concerns about trade competitiveness. Daniel Madar shows that deregulation created a de facto regime of free trade in trucking services. Removing regulatory barriers has enabled Canadian and American carriers to follow the expansion of transborder traffic that began with the Canada–US Free Trade Agreement and continues with NAFTA. The services available with deregulated trucking have also supported sweeping changes in industrial logistics. As transborder traffic has surged, the two countries’ carriers – from billion-dollar corporations to family firms – have exploited the latitude provided by deregulation. This book is a valuable contribution to our understanding of the policy processes and economic conditions that led to trucking deregulation. As a study in public policy formation and the international effects of reform, it will be of interest to students and scholars of political economy, international relations, and transportation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".