Temporary Protection in Times of Crisis: The European Union, Canada, and the Invasion of Ukraine
Bibliographic record
Abstract
The Russian invasion of Ukraine in February 2022 triggered a major displacement crisis. In an unprecedented move, the European Union activated the 2001 Temporary Protection Directive to give those fleeing the conflict temporary protection, marking the first use of the directive in 20 years. Meanwhile, Canada announced its readiness to accept an unlimited number of Ukrainians and launched the Canada–Ukraine Authorization of Emergency Travel to fast-track their arrival. This article compares the policy responses of the EU and Canada to the crisis in Ukraine, focusing on the two temporary protection schemes and differentiating between their overarching goals, policy instruments, and settings. While the policies may seem similar at first, we show that a closer examination reveals underlying disparities, contradictions, and complexities, particularly when analyzing the precise policy instruments and settings. Considering that contemporary policy trajectories are informed by the past, we suggest that while the two programs build on the respective regions’ historical and political contexts, crises also create opportunities for change, raising questions about the future direction of immigration policy in both regions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".