Oma ja võõras toit: 1944. aasta pagulaste toidukogemused ERMi allikate põhjal
Bibliographic record
Abstract
Our Food and Theirs: The 1944 Émigrés’ Food Experience Based on Estonian National Museum Sources In the summer and autumn of 1944, as the Soviet front drew nearer, thousands of Estonians fled across the Baltic Sea to Finland, Sweden and Germany. The total number of those who fled has been estimated at 70,000–90,000 people. Of them, 50,000 Estonians made it to Germany and 28,000 Estonians to Sweden. Later a number of them emigrated further to the US, Canada, Australia and England. In their new adopted homes, the émigrés encountered unaccustomed food ingredients, dishes and food preparation methods. These items varied slightly in all countries, but a theme that was a constant for émigrés when they wrote or talked about their experiences with food was the lack of Estonian-type foods. In the context of people’s everyday expectations and practices, this meant a cultural clash between the habitual practices they carried with them when they became refugees and the experiences in their new homelands. The lack of Estonian food in the local food culture led to the need to prepare the dishes themselves – both everyday staples (such as bread and sauerkraut) and fare associated with special occasions and holidays (head cheese or sült, the beet and potato salad called rosolje and the sweet fruited bread kringel). The food preparers were mainly women, and women also typically also hold a regular job to earn a living for the family. The first generation of émigrés saw Estonian-style food as the food of their homeland, which fostered a direct link to the home they had left; attitudes toward it became increasingly nostalgic. For the first generation of descendants of the émigrés, the flavours and food experiences are a part of home, environment and childhood memories. On the basis of the interviews and answers on questionnaire forms in the museum archive as well as the experiences gained in field work, we can conclude that in everyday practice, Estonian-style food is one of the most vital anchors of identity for émigré Estonian communities. The preparation of Estonian foods, the seeking out of such food from stores and consumption of Estonian food remains for the Estonian diaspora a way to identify as Estonian and adapt into the community of Estonians in their country of location.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".