Embodied geographies of the nation-state : an ethnography of Canada’s response to human smuggling
Bibliographic record
Abstract
This thesis provides a geographical analysis of the response of the Canadian nation-state to human smuggling. I contend that nation-states must be examined in relation to transnational migration and theorized as diverse sets of embodied relationships. As a case study, I conducted an ethnography of the institutional response to the arrival of four boats carrying migrants smuggled from Fujian, China to British Columbia in 1999. I studied the daily work of border enforcement done by civil servants in the federal bureaucracy of Citizenship and Immigration Canada (CIC), as well as the roles played by other institutions in the response to the boats. This "ethnography of the state" led me to theorize the nation-state geographically as a network of employees that interact with a variety of institutions in order to enact immigration policy. I also interviewed employees of other institutions involved in the response to human smuggling, including provincial employees, immigration lawyers, service providers, supra-state organizations, refugee advocates, and media workers. The thesis explores cross-institutional collaboration among them and the resulting decision-making environment in which civil servants design and implement policy. Civil servants practice enforcement according to how and where they "see" human smuggling. My conceptual understanding of state practices relates to these efforts to order transnational migration. Diverse institutional actors negotiate smuggling at a variety of scales. Power relations are visible through discussions of smuggling at some scales, but obscured at others. I "jump scale" through embodiment in order to understand the micro-geographies of the response. This shift in the scale of analysis of the nation-state uncovers different relationships, interests, and negotiations in which state practices are embedded. This approach to geographies of the nation-state considers the time-space relations across which state practices take place, the everyday enactment of policy, the categorization of migrants, and the constitution of borders through governance. I argue that such an approach is key to understanding the relationship between nation-states and smuggled migrants. The findings suggest a re-spatialization of enforcement through which nation-states increasingly practice interception abroad and design stateless: spaces, and in so doing, reconstitute international borders.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".