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Record W2203233677 · doi:10.1353/ces.2015.0056

A Ukrainian Canadian in London: Vladimir J. (Kaye) Kysilewsky and the Ukrainian Bureau, 1931–40

2015· article· en· W2203233677 on OpenAlexvenueaboutno aff
Orest T. Martynowych

Bibliographic record

VenueCanadian ethnic studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianSpanish Civil WarNewspaperPolitical scienceEthnic groupCivil servantsOfficerCivil servantLawEconomic historySociologyPoliticsHistory

Abstract

fetched live from OpenAlex

This paper examines a crucial and formative decade in the life of Vladimir J. (Kaye) Kysilewsky (1896–1976), a Ukrainian-Canadian newspaper editor, lobbyist, university professor, and historian, who is most familiar to Canadian researchers as the federal civil servant responsible for liaison with ethnic groups and the ethnic press during the early years of the Cold War. It argues that the attitudes and methods (Kaye) Kysilewsky brought to his job as a liaison officer were shaped by his experience as director of the Ukrainian Bureau in London. There, during the 1930s, he met and was counselled by a number of British parliamentarians, academics, and journalists, as he attempted to bring to public attention the murderous famine in Soviet Ukraine (which was denied by the Stalinist regime) and as he tried to contend with the Bureau’s obstreperous Ukrainian émigré rivals, in particular the Organization of Ukrainian Nationalists (OUN). Cet article porte sur une décade cruciale et formative dans la vie de Vladimir J. (Kaye) Kysilewsky (1896–1976) qui fut rédacteur en chef de journal, lobbyiste, professeur d’université et historien, et que les chercheurs canadiens connaissent surtout en tant que fonctionnaire fédéral responsable de la liaison avec les groupes et la presse ethniques au cours des premières années de la guerre froide. L’article montre comment l’attitude et les méthodes que (Kaye) Kysilewsky a employées dans son travail d’officier de liaison ont été modelées par son expérience de directeur du Bureau ukrainien à Londres. Là-bas, dans les années 1930, il a rencontré un certain nombre de parlementaires britanniques, d’universitaires et de journalistes et a été conseillés par eux, alors qu’il s’efforçait d’attirer l’attention du public sur la famine meurtrière qui sévissait dans l’Ukraine soviétique (et qui était niée par le régime stalinien) et qu’il essayait de composer avec des rivaux du Bureau, émigrés ukrainiens turbulents, dont particulièrement l’Organisation des Ukrainiens nationalistes (OUN).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.476

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0210.006
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.160
GPT teacher head0.365
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2015
Admission routes2
Has abstractyes

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