Data Laundering Border Violence: Performance Measures and Immigration Enforcement
Bibliographic record
Abstract
On July 8, 2020, the Office of the Auditor General of Canada (OAG) tabled its report on immigration removals, finding that the Canada Border Services Agency (CBSA) fails to enforce deportations in a timely manner and has abysmal record-keeping practices. The report concludes that these failings undermine the integrity of Canada’s immigration system and endangers the public. Critical Accounting scholarship problematizes auditing for legitimizing harmful processes through the guise of scrutiny. The OAG audit of the CBSA overlooks the well-documented systemic abuses of the CBSA in administering migrant detention. The article argues “performance audits” are a governmental technology called data laundering that rationalizes the violence inherent in immigration enforcement. Data laundering obscures the fact that policing migration depends on broad discretionary powers, leading to opaque and inconsistent data practices. “Laundering” signals auditing’s inability to be sufficiently adversarial with a sector of law enforcement whose poor data-keeping practices maintains an illusion of recordkeeping as a form of power. Audit dependence on quantitative forms of data increases violence against immigrants; when violent deportation and detention measures are quantified, this presumes an acceptable ledger of force that accounts for, and in so doing legitimizes, state enactment of violence upon vulnerable people.
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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 | 0.045 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".