A Fully-Fledged and Finely Functioning Fiddle: Humour and 'The Uke-Cree Fiddler'
Bibliographic record
Abstract
I first met Arnie Strynadka, the “The Uke-Cree Fiddler,” while conducting field research at the Vegreville Ukrainian Pysanka Festival in Alberta in 2002. When I stopped at Arnie’s booth to chat, he told me that he was the child of a marriage between a Cree woman and a man born of Ukrainian immigrants. I had already spent a decade researching and writing about Ukrainian culture and identity, and a lifetime as a Ukrainian cultural, music and dance performer and enthusiast. Yet it was the first I had ever heard of family relations between Aboriginal people and Ukrainian immigrants. While there is evidence of relations between Aboriginal people and Ukrainians, almost no attention has been paid to this matter in scholarship or public memory. I hope to attend to this gap in the literature, through research I have recently begun on the expressive culture of people who claim both Aboriginal and Ukrainian descent and a broader musical legacy of Aboriginal-Ukrainian encounters in Canada. This essay emerges from what I have been learning in the earliest stages of this new research project, especially concerning humour as it arose in conversations with fiddler Arnie Strynadka. For, as the photograph below shows, Arnie has made much of a comedic persona. Humour is often storied as part of Aboriginal culture, as Drew Hayden Taylor (2005a) and other writers have discussed in Me Funny. Further, as Philip Deloria has written, humour often arises from the juxtaposition between the expected and the unexpected (2004:3)—as in the encounter I describe below.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".