Data Laundering Border Violence: Performance Measures and Immigration Enforcement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it