Affective politics, effective borders : news media events and the governmental formation of Canadian immigration policy
Bibliographic record
Abstract
In the wake of the widespread media focus on the securitization of Canadian immigration policy and its harmonization in a North American "smart border" regime, this thesis examines the role that large-scale news media events around immigration and refugee asylum play in the formation of Canadian immigration policy. Two crucial news events in particular, the 1999 landings of Fujian Chinese migrants off the coast of British Columbia, and the focus on the ostensibly porous Canadian border following the September 2001 attacks in the United States, are analyzed in terms of their impacts on the introduction and intensification of trafficking and security policies, as well as selection, interdiction and enforcement (deportation/detention) practices. This project draws on and reworks theories of biopolitics and governmentality, along with the emerging literature on affect in cultural studies, to consider the effects that spectacular news events around migration have on the governmental formation of immigration policies. Methodologically, it draws on elements of interpretive and discourse analysis, qualitative interviews, along with an approach developed to account for the affective dimensions of news events in everyday life. The thesis argues that these highly affective news media events have been crucial to the formation of an increasingly racialized and sexualized biopolitics of migration focused on the preventative targeting of "risky" migrant bodies and their precarious movements and labour, as crystallized in the recent Immigration and Refugee Protection Act (2002) and the Safe Third Country Agreement (2004)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".