Slavic and East European Folkloristics at the Canadian Museum of Civilization: An Overview
Bibliographic record
Abstract
In keeping with the growing recognition of Canada's multicultural profile and its concomitant discovery of terrae incognitae, the Canadian Museum of Civilization (nee National Museum of Canada, and later National Museum of Man), Canada's national museum of anthropology, began the earnest collection, documentation and study of Slavic and East European folklore materials in the early I%O's thanks in most part to the pioneering work of Kenneth Peacock. (Earlier, incidental Slavic and East European materials appear sporadically in the Museum's collections of such figures as M. Barbeau and L. Boulton.) From the outset these efforts emphasized the new Canadian experience. (In any case, conditions in Eastern Europe at that time discouraged the undertaking of credible folklore research in the homelands.) This in turn prompted the accumulation of complementary primary data that, from a global perspective, now serve to enhance our appreciation of Slavic and East European folklore. In the course of these early activities, the traditional canons whether Slavic, Baltic, Yiddish or Finno-Ugric -usually served as launching pads as researchers and field workers (such as J.L. Perkowski, V.R Tilney, L. Degh, M.T.lS.M.G. Salo, B. Kirshenblatt-Gimblett, and others) struggled either to salvage from the past or to cross the gaps that divided classical Old Country lore from North American realities. After all, what does one make of such seeming aberrations as Polish jokes that are not even delivered in Polish, or Ukrainian village harvest songs performed on the cemented balcony of a highrise condominium in·downtown Toronto? Among the pearls was the latter-day discovery of an unpublished collection of Russian incantations recorded (on paper) among Canada's newly arrived Doukhobor immigrants a century ago by one of V.I. Lenin's more illustrious cronies, V.D. Bonch-Bruevich. (His original field notations, located in Russian archives, are currently being prepared for publication.) The inital focus on oral lore (folksongs dominated this phase) soon mushroomed to include attendant phenomena. Ritual, belief: art and material culture emerged as areas that also warranted attention if the Museum was to mount well-researched and comprehensive exhibitions on the nation's East European legacies. This trend is reflected in the multi-disciplinary study of a Hungarian settlement on the prairies (the Bekevar project) in the 1970s, two exhibitions marking the centenaries of particular Slavic communities in Canada (Art and Ethnicity: the Ukrainian Tradition in 22
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".