Leveraging the Canada-Ukraine authorization for emergency travel (CUAET) program to facilitate talent mobility
Bibliographic record
Abstract
This paper examines the potential for leveraging the Canada-Ukraine Authorization for Emergency Travel (CUAET) Program to facilitate the mobility of Ukrainian Information Technology (IT) professionals from Ukraine into Canada. The paper revisits the situation following Russia’s invasion of Crimea (2014), the redirection of specific talent flows prior and during the pandemic and aims to assess Canada's attractiveness as a talent destination. It focuses on the implications of the war on Canada's attraction and retention of IT skilled workers from Ukraine, and whether the measures implemented by the Government of Canada were effective in providing incentives. Ultimately, this paper considers the CUAET, which was recently extended to continue assisting Ukrainians fleeing the war, as a model to replicate and utilize when responding to displacement crises – thus adding it to Canada’s migration diplomacy and foreign policy toolkit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".