Dietary acculturation and the nutrition transition: an overviewThis is one of a selection of papers published in the CSCN–CSNS 2009 Conference, entitled Can we identify culture-specific healthful dietary patterns among diverse populations undergoing nutrition transition?This paper is being published without benefit of author’s corrections.
Bibliographic record
Abstract
This article provides an overview of the phenomena of "dietary acculturation" and the "nutrition transition". The term dietary acculturation is defined, and a proposed model for how dietary acculturation occurs is presented. Various approaches to assessing dietary acculturation in research studies are discussed, and a research-driven discussion of the possible consequences of dietary acculturation on diet and health is presented. The concept of the nutrition transition and the importance of understanding this phenomenon are presented. Specifically, the term nutrition transition is defined, highlighting characteristic dietary changes, as well as shifts in physical activity and obesity trends. The paradox of continued high rates of malnutrition in developing countries and a new emergence of dietary excess, sedentary behavior, obesity, and other chronic diseases in middle- and upper-class persons in these countries will also be addressed. The article concludes with suggestions for future research in the areas of dietary acculturation and the nutrition transition.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".