Untangling Deportation Law from National Security: The Pandemic Calls for a Softer Touch
Bibliographic record
Abstract
There is a significant overlap between national security law and deportation law. Non-citizens, even refugees and permanent residents, found to be terrorists, members of organized criminal groups, spies, criminals, or money launders can be declared “inadmissible” and deported from Canada (IRPA, ss. 34–40). For the government, deportation is a security-enforcement tool. As Public Safety Canada explains, “immigration removal is an integral part of the [Canada Border Services Agency’s (CBSA)] security mandate” (CBSA 2020a). Moreover, deportation is an often-used tool. Compared to the criminal system, immigration adjudicators are regularly called upon to grapple with terrorism cases. A recent study showed that between 2004 and 2018, there were only 15 criminal trials based on terrorism charges (Nesbitt and Hagg 2020, 597). In contrast, the Immigration and Refugee Board adjudicated 123 national security and terrorism deportation cases in 2018 alone (Immigration and Refugee Board 2021). There is a practical reason for the national security community to concern itself with what happens in the deportation space: the immigration tribunals adjudicate exponentially more national security cases than do the criminal courts.
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.
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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.025 | 0.077 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.036 | 0.071 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".