Bibliographic record
Abstract
The fundamental purpose of this book is to engage readers to appreciate the empirical evidence demonstrating disparities in access to healthy affordable foods across the United States, and that these disparities may explain food consumption patterns for some Americans as well as potential risk for diet-related illness. Furthermore, the book describes the current body of research that has investigated these associations and presents the methodological issues pertinent to this area of public health specifically. Evidence from these studies is put into the context of current and historical American food policies that have supported the existing food retail market, including the production and retailing of foods within the United States and the ways in which the consolidation of the U.S. food system has affected Americans. Although the focus of this book pertains to local food environments within the United States, similar issues regarding access to food are concurrently taking place outside the United States. For instance, research on this subject has been conducted in Europe, Australia, and Canada. Therefore, research conducted regarding local food environments in Canada has been included as a point of comparison. In Chapters 4 through 8, methods and the current state of knowledge regarding the factors associated with disparities between local food environments, the effect of these disparities on the diets of residents within those communities, and finally the impact local food environments have on diet-related health outcomes, such as obesity, are discussed. In the final chapters, we describe solutions garnered to minimize local food environment inequalities that are currently being conducted by federal, state, and local government agencies in the United States to reduce imbalances between local food environments. Within all chapters, readers are encouraged to critically consider the current research methods as well as recent programs and policies that aim to address local food environments. This is an emerging area of public health that requires a range of multidisciplinary experts from fields such as nutrition, business, city planning, policy, epidemiology, health behavior, and geography to conceive, implement, and evaluate environmental changes that will promote health for all Americans.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.452 | 0.320 |
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".