Bibliographic record
Abstract
The article provides a brief overview of the history of Ukrainian emigration to Canada. In particular, the historical context and features of different waves of emigration, starting from 1891 to the present day, are discussed. Particular attention is paid to the choice of residence by newcomers and the factors that influenced this decision. The author traces how immigration policy, as well as employment opportunities and the presence of relatives and friends, affected the settlement of Ukrainians in Canada. In particular, the article provides information about the first wave of immigration, when the government promised 64 hectares of land as homesteads for a nominal fee of $10. As a result of this policy, Ukrainian settlements were founded in Manitoba, Saskatchewan, and Alberta. The article highlights the historical background of the second and third waves of emigration and the peculiarities of Canada's policy at the time, which resulted in a significant number of Ukrainians settling not only in the West of the country, but also in Ontario and Quebec. The article notes that there is no consensus among scholars on whether the arrival of Ukrainians from Poland, Yugoslavia, and other Central European countries in the 1980s should be distinguished as a separate wave. It also discusses emigration after the collapse of the Soviet Union and the resettlement of Ukrainians in Canada over the past 30 years. Despite the fact that in the twenty-first century Ukrainians live almost all over the country, Manitoba, Alberta, and Saskatchewan have the highest percentage of people in Canada who declare their Ukrainian ancestry. Special attention is paid to the wave of emigration caused by russia's full-scale war against Ukraine. Thanks to a special program of the Government of Canada, Canada-Ukraine authorization for emergency travel, 298 thousand Ukrainians arrived in Canada between March 17, 2022 and April 1, 2024. The article presents the results of surveys of newcomers and information about the factors that influenced their choice of place of residence.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".