In the “Service” of Migrants: The Temporary Resident Biometrics Project and the Economization of Migrant Labor in Canada
Bibliographic record
Abstract
Since 1993, the Canadian government has used biometric screening to identify migrants crossing in and out of Canadian territory. Recently, however, the government has sought to enhance the scope of biometric screening through a number of proposed acts and programs. Of particular interest is the 2013 Temporary Resident Biometrics Project. The Project will require that foreign nationals provide enhanced biometric details, including fingerprints and facial capture, which will be shared with other governmental departments such as the Royal Canadian Mounted Police and Canada Border Services Agency. Although the Canadian government has made reference to increasing vulnerabilities in their capacities of identification and verification, it is unclear exactly why the enhanced surveillance and governance of noncitizens is necessary. We argue that the increasing deployment of biometrics is part of a larger global program designed to promote and manage temporary, short-term labor. We explore this use of biometrics by contextualizing the arguments advanced by the Temporary Resident Biometrics Project within the larger neoliberal discourse of market uncertainty and risk management. The working theory draws from Callon's thesis of hybrid forums to investigate the economization of biometric screening and, in turn, how the state's relationship and obligations to noncitizens are increasingly defined through the rhetoric of a market-driven economized service. A brief overview of the Canada–U.S. NEXUS program serves to demonstrate the Project's relevance to understanding how surveillance is being used to manage global flows of labor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".