Making and Unmaking Collective Memory through Food: A Case Study of Windsor, Ontario’s Yugoslav Diaspora
Bibliographic record
Abstract
The preparation and consumption of food is not merely a physical act, but a deeply social one, conveying cultural meaning that functions to tie us to our identity and profoundly influence our memory. Drawing upon interviews done with members of Windsor’s Yugoslav diaspora community, this research seeks to explore the ways in which this group has negotiated its collective memory within the host society through the use of food. I identify four central aspects of food’s relation to collective memory within the diaspora. First, the use of food as a means of connection to the homeland, and therefore, to collective memory. Second, the use of traditional foods as a means of gaining acceptance and visibility through the exploitation of collective memory. Next, the alteration of traditional foods as a means of gaining acceptance and (in)visibility through a form of selective forgetting. Finally, the rejection of Yugoslav culture as a means of assimilating and thus of forgetting. Taken in combination, these various approaches provide a multifaceted, comprehensive account of how food acts in relation to memory and forgetting. The emerging field of food history, with its unique ability to grant insights into cultural memories of both the private and public sphere, opens the door to a fuller, richer understanding of the dynamics of migrant life.
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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 | 0.001 | 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 | 0.023 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".