Bibliographic record
Abstract
An international ensemble of folklore scholars looks at varied ways in which national and ethnic groups have traditionally and creatively used imagined states of existence-some idealizations, some demonizations-in the construction of identities for themselves and for others. Drawing on oral traditions, especially as represented in traditional ballads, broadsides, and tale collections, the contributors consider fertile landscapes of the mind where utopias overflow with bliss and abundance, stereotyped national and ethnic caricatures define the lives of "others," nostalgia glorifies home and occupation, and idealized and mythological animals serve as cultural icons and guideposts to harmonious social life. Italian Canadian Luisa Del Giudice looks at the rich Italian variants of the traditional gastronomic utopia called Il Paese di Cuccagna, the Land of Cockaigne, "a mythic land of plenty where rivers run with 'milk and honey' (wine, beer, coffee, or rum), food falls like manna from heaven, work is banished, and no one ever grows old" and considers its persistence in immigrant worldview. From New Delhi, Sadhana Naithani examines the "preface-d space" that as India, colonial British authors imagined and passed on to readers in formulaic prefaces to collections of Indian folklore. Reimund Kvideland, of Norway, and Gerald Porter, an English scholar teaching in Finland, show how nineteenth-century Norwegian and English railway navvies (itinerant laborers) idealized their low-status occupations in song. In a second essay, Gerald Porter demonstrates through broadside ballad texts the role of caricatures of the Welsh, Scottish, and Irish in constructing "Englishness." Turks were among the "others" Germans demonized, as Tom Cheesman, who teaches in Wales, explains in his paper on their historical representations in German street ballads. Cozette Griffin-Kremer of France paints a sweeping picture of the landscape of the mind that written and popular traditions of Scotland, Ireland, and Wales built around bovine bodies, the human-cow partnership, and the mysteries of domestication, thereby providing conceptions of transcendence of the human condition. Finally, Vaira Freibergs, a scholar and the current president of Latvia, explains the images of longing for idealized childhood homes that married women, exiled by a patrilocal culture, expressed in Latvian folksong.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".