Differences and Similarities in Attitudes towards Intellectual and Visual Culture within the Ukrainian-Canadian Community in Edmonton, Alberta
Bibliographic record
Abstract
Ukrainian-Canadians are a relatively well-established group in Canada. This paper draws on data gathered from ten interviews about ethnic identity discourses which I conducted with new and established members of the Ukrainian-Canadian community in Edmonton, Alberta. Using critical discourse analysis, I investigate the responses to nine of the original thirty-seven interview questions, which included two ranking questions; these questions inquired about participants’ opinions and evaluations of [Ukrainian] literature, language, music and important “kinds” and aspects of culture. Responses exposed some of the similarities and differences in attitudes the two groups held towards intellectual and visual culture, highlighting the evolving nature of this community, and providing detail that enhances understanding of these attitudes. I present key arguments as to why these similarities and differences may, at least in part, correlate to the unique socio-cultural environments in which each group has been developing culture since Ukraine’s Independence. In particular, I posit that “the linguistic factor” (a term I use to summarize the interconnected influence that language, literature, and linguistic ability have on each other) is one of the most salient forces in shaping and informing these similarities and differences in attitudes towards intellectual and visual culture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".