Bibliographic record
Abstract
Millions of Ukrainians have been displaced since Russia launched a full-scale invasion of Ukraine earlier this year. Yet, Canada has only received a small number of Ukrainian refugees compared to other countries, despite citizens’ overwhelming willingness to receive them. Canadian immigration policies are the issue and need to be revised to allow faster and easier access for Ukrainians. While the federal government has launched a program to fast-track Ukrainian refugees, Canada-Ukraine Authorization for Emergency Travel (CUAET), statistics show that it isn’t enough. Under CUAET, visas and temporary residency permits are expedited for Ukrainians and their families. While the number of applications is increasing, the program’s number of approvals is slowing. That Canadian visa policies are the cause of the comparatively low number of Ukrainian arrivals is supported by empirical evidence from Ireland and the United Kingdom. Both countries are similar in ways that attract displaced Ukrainians, but only Ireland lifted its visa requirements in response to the war; the U.K. has maintained a Canada-like visa process. As a result, approximately 13 times the number of Ukrainian refugees per capita arrived in Ireland than in the U.K. during the first two months of the invasion. The visa requirements in Canada have, in a similar way, stymied the arrival of Ukrainian refugees. There are several options that could be implemented to increase the flow of Ukrainian refugees to Canada. The first, visa-free travel, is already in place in Ireland and has proven very successful, allowing an additional estimated 23,000 arrivals above what might have been expected under pre-war Irish visa policies. The second is a visa-on-arrival system. Security checks are done at the port-of-entry and a visa can be issued onsite. The third option is a hybrid of CUAET and options 1 or 2 — allow visa-free travel or a visa-on-arrival program for Ukrainians with proper documentation while those without documentation go through CUAET. This will speed up the CUAET process since documented refugees will not be part of the list anymore. National security concerns are one of the reasons for Canada’s more strict immigration policies. However, research shows that foreign agents are less likely to infiltrate strong, stable countries through refugee streams. As well, refugees tend to be targets of espionage themselves and can be a valuable source of intelligence. Canada could invest more in counterespionage to help identify high-risk entries and mitigate the small risk of infiltration. Approximately 80 per cent of Canadians are willing for this country to receive Ukrainian refugees. The chance of refugee-related espionage is minuscule while the impact of Canada’s humanitarianism is huge. The federal government needs to consider this trade-off and amend immigration policies for Ukrainian war refugees.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".