Bibliographic record
Abstract
Ukrainian ethnography has been a large, diffuse field of activity in Canada, with several identifiable threads. The field’s significance has been primarily cumulative rather than evident in individual field-changing works. Robert Klymasz’s PhD dissertation (“Ukrainian Folklore in Canada,” 1971) on continuity and change in Ukrainian Canadian culture is the main exception. Some studies have dealt with traditional culture in Ukraine, but the mainstream of Ukrainian Canadian ethnography has focused on Ukrainian cultural activities documented in Canada itself. Within these Canadian materials, many scholars have allowed for, and even celebrated, the processes of adaptation, hybridity, and creativity in Ukrainian Canadian culture. Ukrainian Canadian ethnography has been strongly integrated with North American scholarship in general, but until recently it was poorly connected with folkloristics and ethnology in Ukraine. Canadian ethnography has potential to contribute to Ukraine’s ethnology and folkloristics through its nuanced elaboration of the importance of context and its documentation of processes of cultural change and hybridity, urban traditions, ethnic identity and revival, and multicultural relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".