Bibliographic record
Abstract
The United States-Canada political boundary has long been praised as the most extensive peaceful international border in the world. However, this reputation has shifted considerably in recent years. The US has strengthened its northern border security infrastructure at and between ports of entry, hiring new enforcement personnel and upgrading technology to respond to potential threats emerging from Canada. I analyze this change of United States policy and practice by focusing on one US borderland context: northwestern Washington’s Olympic Peninsula. My analysis is driven by the following questions: (1) how do security tactics respond to specific cross-border threats; and (2) why are some Olympic Peninsula residents contesting securitization? In working through these questions, my objective is to foreground everyday enforcement encounters as constitutive of geopolitics – in other words, to identify how the people and places of the peninsula both impact and are impacted by border practices. I argue that national security tactics make borderland residents on the Olympic Peninsula insecure. More specifically, border policing practices carried out in remote inland areas make both law enforcement officers and peninsula residents targeted for policing feel unsafe, without clearly responding to precise cross-border threats. In response, grassroots groups have organized, questioning the relationship between the mission and everyday practices of the United States Border Patrol in rural areas of the US northern border. Analytically, I draw from materially-grounded feminist theory, basing my argument on two conceptual points of departure – first, that security is embodied; and second, that inequalities are interconnected. Drawing insights from the contestations to securitization on the peninsula, I conclude with a consideration of how national security tactics could be more accountable to the wellbeing of borderland residents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".