Beyond Dietary Acculturation: How Latina Immigrants Navigate Exclusionary Systems to Feed Their Families
Bibliographic record
Abstract
Abstract Previous studies of dietary acculturation explain how immigrants’ diets change over time, but they don't tell us why. In response to calls for additional research on the complex social processes that shape health disparities, this study uses an intersectional approach to examine the role of food in the daily lives of 23 Latina immigrants living in North Carolina. Our findings, based on semi-structured interviews conducted over a five-year period, refute the idea of a unidirectional process in which immigrants abandon dietary customs from their home countries. Instead, we show how food decisions are complex, contradictory, and contextual. Latina immigrant mothers embraced and resisted parts of dominant food cultures. They strategically took risks and made tradeoffs to ensure that their families had enough food and the right kinds of food. However, political and economic structures limited their access to food and impeded their ability to autonomously make food decisions. We argue that an unequal and industrialized food system, restrictive and punitive immigration policies, and narrowly-defined food assistance programs infringe on immigrants’ ability to feed their families. By excluding and othering immigrant families, these structures reduce immigrants’ autonomy and perpetuate inequalities, contributing to what previous studies have described as dietary acculturation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".