Not to be used without the permission of the authors
Bibliographic record
Abstract
Food is more than a basic source of nutrients; it is also a key component of our culture, central to our sense of identity. Identities, however, are not fixed social constructs, but constructed and reconstructed within given social formations reflecting the existing and imagined structural constraints and lived experiences of subjects. This paper examines the dynamic relationships among food, social identity and the immigrant experience. As a culturally and spatially transitional stage, the immigration process introduces possibilities for change, as well as resistance to new habits, new behaviours, and new cultural experiences. These changes, in turn affect our physical and mental health, our perceptions of self, and our relations with others. This paper offers some analytical insights into this cultural transition and its impacts on identity drawn from the literature on food and identity. It also examines the impacts of the social constraints of food security among a group of immigrants in Toronto in order to evaluate the complex dynamics of identity reconstruction. It is argued that both the politics of equality and the politics of recognition are relevant to immigrant food security. Food, Culture and Identity: To survive we need to eat. Yet, food is more than a source of energy and nutrients
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.649 | 0.498 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".