Deciding To Deport: Considering Collateral Immigration Consequences in Ontario Based Courts
Bibliographic record
Abstract
This dissertation unpacks how migrants with criminal convictions are governed across the immigration and criminal punishment systems in Canada. The analysis traces processes that span these two domains and contribute to the program of removal for criminality. I draw from debates on immigration law to provide a historical review of legislation governing deportation from 1869 to 2002, when the current Immigration and Refugee Protection Act was enacted. I trace the rationalizations for amendments to this legislation introduced by the Faster Removal of Foreign Criminals Act (FRFCA) in 2013. The analysis then shifts to consider how knowledge of deportation for conviction and sentence impacts processes in the criminal system. I review how knowledge of the program of removal is accessed and deployed by court actors. I also examine the effects of the consideration of immigration consequences on sentencing outcomes. I reviewed 188 cases and conducted 13 interviews with judges, defence counsel and immigration lawyers in support of this effort.\n \nThis research reveals the complex exchange of knowledges, actors, and technologies across the assemblages of the immigration and criminal punishment systems. I demonstrate the clear reliance of government officials on racialized ideas of citizenship in legislating deportation. I establish the repetition of racist logics over time, including in support of the FRFCA. I then demonstrate the interweaving of logics of race with knowledges of court processes. I reveal that amendments to the program of removal introduced via the FRFCA were justified as delimiting the authority of court actors to consider deportation. I also show that judges continue to contemplate the collateral immigration consequences of sentencing post-passage of the FRFCA. \n\nI argue that judicial discretion is shaped by a myriad of factors, including knowledge of legal principles, information specific to the geographical location of the court, and the background of the judge. The interweaving of this information is shown to have varied effects for the program of removal, with some migrants being protected from deportation by court actors through sentencing. Opportunities for resistance to removal are thus argued to exist in the points of tension in implementation of the program of removal across the immigration and criminal systems in Canada. This work also reveals that racialized knowledge guides judicial decision making on migrant sentence. Racialized migrants are produced through these processes as outside of the citizenry of Canada. The work thus confirms that the immigration and criminal punishment systems in Canada function in tandem to support racial governance.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".