Петро Саварин – будівничий українського життя в Канаді(Petro Savaryn as the constructor of Ukrainian life in Canada)
Bibliographic record
Abstract
У тезах автором висвітлено життєвий шлях видатного діяча української громади у Канаді, уродженця Тернопільщини Петра Саварина. Активний учасник суспільно-політичного життя, П. Саварин долучився до організації та розвитку багатьох українських організацій. Участь у політичному житті Канади, членство у консервативній партії, докладання значних зусиль у розробку теорії мультикультуралізму дали змогу останньому лобіювати інтересів українців на канадській землі. Окремо наголошено на ролі П. Саварина у розбудові української науки – участь у створенні Канадського інституту українських студій, виданні англомовної «Енциклопедії України». (The author clarifies the life journey of Petro Savaryn, an outstanding activist of the Ukrainian community in Canada, a native of Ternopil region. As an active contributor to socio-political life, Petro Savaryn took part in the foundation and development of many Ukrainian organizations. Involvement in the political life of Canada, membership in the Conservative party, and great efforts to the development of the multiculturalism theory made lobbying of the Ukrainians’ interests on the territory of Canada possible for Petro Savaryn. Moreover, the paper stresses on his role in development of the Ukrainian science, as he took part in the founding of the Canadian Institute of Ukrainian Studies and in publishing Encyclopaedia of Ukraine in English.)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".