“Becoming a Finn” in the United States : the representations of foodways, language, sports, and music in the processes of identity formation in contemporary Finnish-American fiction
Bibliographic record
Abstract
This dissertation explores the construction of Finnish-American identities in the United States in Finnish-American fiction published in the late 20th and early 21st centuries. Despite the high number of titles, the literature produced by the descendants of Finnish immigrants is understudied in comparison to other ethnic and immigrant literatures in the USA and Canada. The present dissertation contributes to filling this gap. Drawing on the theoretical framework of transcultural and transnational studies, especially Mary Louise Pratt’s (1992/2008) concepts of transculturation and a contact zone, the study raises the question what roles four cultural practices – foodways, language, sports, and music – play in constructing the characters’ Finnish-American identities through creative cultural fusion. (1) Foodways are analyzed in Lauri Anderson’s collections of short stories Heikki Heikkinen (1995), Misery Bay (2002), and Back to Misery Bay (2007); (2) the roles of languages in creating “American Plus Finnish” transcultural identity in Patricia Eilola’s novels of formation A Finntown of the Heart (1998) and A Finntown of the Soul (2008); (3) sports and transnational and regional identity are analyzed in the collection Heikki Heikkinen by Anderson and the novel Welcome to Shadow Lake (1996) by Martin Koskela; and (4) music in Welcome to Shadow Lake by Koskela. The study discusses (1) old and new foodways in relation to ethnocultural boundaries, dynamic intergenerational relations, and strong regional Michigan’s Finnish identities; (2) illuminates languages, accents, and names in self-positioning in Finnish- and English-speaking worlds; (3) highlights the Olympic games, winter sports, and the US sports in Michigan’s Finnish-American identity-building; and (4) illustrates the significance of live and recorded music in relation to the identity formation of US-born Finnish generations. The analysis of these four practices emphasizes the transcultural and transnational dynamics of identity formation as taking place through constant encounter, negotiation, and mutual influence of Finnish, American, and Finnish-American cultural elements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".