Shifting Boundaries of Control: The Quebec and Vermont-New York Border in the Trump Era and Beyond
Bibliographic record
Abstract
This article reviews the shifting practices of control along the border New York–Vermont bor-der in the United States and Quebec in Canada during and since the election of the Trump admin-istration in the United States. The authors argue that this period saw an increase in detention, deportation and securitization on both sides of the border, despite the differences in the politi-cal orientations of the Canadian and U.S. governments. Drawing on recent developments in border theory, the article explores the ways in which the northern U.S. border has become increasingly politicized and securitized. The Trump administration’s anti-migrant policies led to a rapid increase in the numbers of asylum seekers crossing the Canada-U.S. border into Quebec in an irregular fashion at Roxham Road in northern New York state. The intense political re-sponse to this situation in Canada and especially Quebec eventually resulted in the renegotia-tion of the Safe Third Country Agreement between the two countries. In the same period, in the northern United States, there was an increase in surveillance and targeting of migrants through the enforcement of international checkpoints 100 miles south of the territorial border. The arti-cle demonstrates how both states attempted to contain the movements of “undesirables,” thus restricting the mobility of certain individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".