MétaCan
Menu
Back to cohort
Record W3023217305 · doi:10.17467/ceemr.2020.01

Helping the Homeland in Troubled Times: Advocacy by Canada’s Ukrainian Diaspora in the Context of Regime Change and War in Ukraine

2020· article· en· W3023217305 on OpenAlexaffabout
Klavdia Tatar

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUkrainianDiasporaHomelandContext (archaeology)Political scienceHistoryLawArchaeologyPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.240
GPT teacher head0.489
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicDiaspora, migration, transnational identityFrench-language works237,207