Helping the Homeland in Troubled Times: Advocacy by Canada’s Ukrainian Diaspora in the Context of Regime Change and War in Ukraine
Bibliographic record
Abstract
This paper analyses diaspora advocacy on behalf of Ukraine as practiced by a particular diaspora group, Ukrainian Canadians, in a period of high volatility in Ukraine: from the EuroMaidan protests to the Russian invasion of Eastern Ukraine. This article seeks to add to the debate on how conflict in the homeland affects a diaspora’s mobilisation and advocacy patterns. I argue that the Maidan and the war played an important role not only in mobilising and uniting disparate diaspora communities in Canada but also in producing new advocacy strategies and increasing the diaspora’s political visibility. The paper begins by mapping out the diaspora players engaged in pro-Ukraine advocacy in Canada. It is followed by an analysis of the diaspora’s patterns of mobilisation and a discussion of actual advocacy outcomes. The second part of the paper investigates successes in the diaspora’s post-Maidan communication strategies. Evidence indicates that the diaspora’s advocacy from Canada not only brought much-needed assistance to Ukraine but also contributed to strengthening its own image as an influential player. Finally, the paper suggests that political events in the homeland can serve as a mobilising factor but produce effective advocacy only when a diaspora has already achieved a high level of organisational capacity and created well-established channels via which to lobby for homeland interests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".