Fighting Human Smuggling or Criminalizing Refugees? Regimes of Justification in and around R v Appulonappa
Bibliographic record
Abstract
Abstract Following the arrival of the MVOcean Ladyin 2009, four men were charged with human smuggling under s. 117 of theImmigration and Refugee Protection Actfor having helped Sri Lankan asylum seekers reach Canada. Section 117 made it a criminal offence to aid and abet the unauthorized entry of asylum seekers, including when this was done for humanitarian reasons, to help family members, or as a matter of mutual aid. The case made its way to the Supreme Court and, in 2015, the court ruled inR v Appulonappathat s. 117 was too broad, potentially criminalizing humanitarian workers and family members who help transport asylum seekers, and should be interpreted in a strict manner. This article draws from pragmatic sociology to study the regimes of justification mobilized by various actors involved in, and around,R v Appulonappabetween 2009 and 2015. It focuses on two sites of contestation that crystalized around divergent conceptions of fairness and safety, discussing how competing regimes of justification were used to advance stakeholder’s positions.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.037 | 0.072 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".