Ukrainian Diaspora in Canada: Methodology and Practice of Research
Bibliographic record
Abstract
The aims of this paper are the estimation of demographic and socio-economic characteristics of the Ukrainian diaspora in Canada, and the description of methodological problems arising in the process of its implementation.Population censuses, which represent the entire population of Canada, including those of Ukrainian origin, are considered as the most comprehensive, informative and reliable source of information on Ukrainians in Canada.The paper shows that methodological problems are caused both by the specifics of the ethnic group and by changes in census procedures.The article focuses on the changes in Canadian ethnic terminology in early population censuses.It is noted that census statistics experienced a significant change in the definition of ethnicity.Starting in 1986, the question on ethnicity includes descendants of mixed ethnic marriages; this makes it impossible to compare numbers of Ukrainians in previous years.A map of the distribution of ethnic Ukrainians was constructed based on data from the 2016 Canadian population census.The paper analyzes changes in the age and sex structures of the population of Ukrainian origin and their language characteristics.Trends in the number of persons of Ukrainian origin, who consider Ukrainian as their mother tongue, are assessed.We show that the proportion of persons over 65 years of age with Ukrainian mother tongue is constantly increasing, while the respective proportion of younger persons is decreasing.The number of Canadian Ukrainians who know Ukrainian is decreasing.Their distribution by age groups has a clearly defined right-sided asymmetry, i.e., the top of the curve is constantly shifting towards the older age groups.The article shows that the overwhelming majority (90 % and more) of ethnic Ukrainians know and use English, while French is much less prevalent.The proportion ISSN 2072-9480.Демографія та соціальна економіка, 2019, № 3 (37) of persons who know both official languages is increasing and this proportion among ethnic Ukrainians is similar to the proportion for the whole population of Canada.The proportion of persons who do not speak any of the official language is decreasing and is less than one percent.We show that Ukrainians have fairly good socio-economic positions relative to the Canadian population as an ethnic group.For example, their position in the labor market is in some cases more advantageous compared to the national average.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.042 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".