Narratives of Ukrainian diaspora mobilization in Canada: a discourse analysis
Bibliographic record
Abstract
The largest Ukrainian diaspora is in Canada, however, after the start of massive Russian aggression in 2022, the figures for Canadian assistance to Ukraine cannot compete even with the five countries supplying assistance to Ukraine in the current situation. It is still unclear why the potential opportunity to influence Canada’s domestic and foreign policy was lost on the part of the Ukrainian diaspora, but in this thesis, we will look at how the mobilization of the Ukrainian diaspora took place in the light of unfavorable events in their native state. The purpose of this study is to find out how the Ukrainian diaspora mobilized its supporters in the period from 2014 to 2022. This study conducts a critical discourse analysis of the narratives that shaped the discourse of the presidents of the Ukrainian Canadian Congress and contributed to the mobilization of the Ukrainian diaspora in Canada. The thesis analyzes text and video reports of the presidents of the Ukrainian Canadian Congress and focuses on 3 narratives: “Struggle for Freedom and Dignity” narrative, “Holodomor and Canada's First National Internment Operations” narrative, and “Ethnic and Organizational Cohesion” narrative. Thus, during the period under study, the participants of the Ukrainian Canadian Congress mainly used the “Struggle for freedom and dignity” narrative and the “Ethnic and organizational cohesion” narrative; they can rightfully be considered the dominant and formative narratives for the mobilization of the Ukrainian diaspora. The “Holodomor and Canada's First National Internment Operations” narrative was practically not used in the discourse to mobilize diaspora supporters.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".