The Impact of the Humanitarian Nationalism on the Perception of Refugees in Canada
Bibliographic record
Abstract
Russia's invasion of Ukraine in February 2022 caused a refugee crisis. Canada responded by implementing the Canada-Ukraine Authorization for Emergency Travel (CUAET) program, to swiftly relocate a large number of Ukrainian refugees from Europe. The CUAET program provided Ukrainians with exceptional generosity, which contrasts with the support typically offered to other refugee groups. CUAET's adoption has gained widespread attention as Former Minister of Immigration Sean Frazer claimed it would establish a precedent for addressing future refugee challenges. Currently, there is a lack of understanding regarding the CUAET program's adaptability to other refugee groups. The CUAET program was extended to Ukrainians due to a strong sense of "Humanitarian Nationalism" in Canada (Hyndman, 2023). Humanitarian nationalism is the national commitment to supporting refugees, emphasizing collective responsibility for assisting them. This phenomenon was responsible for Canada's outpouring of support for Ukrainians. However, not all refugees benefit from this sentiment, as government and public support are not extended to all refugees in the same ways. Drawing on Hyndman's concept, this research aims to explore the specific circumstances surrounding Ukrainian refugees that led to the creation of the CUAET program and assess the potential applicability of extending Canada's CUAET program to other refugee groups. This can be achieved through an analysis of Syrian and Ukrainian resettlements to Canada within the framework of "Humanitarian Nationalism."
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| 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".