Food Literacy: Definition and Framework for Action
Bibliographic record
Abstract
The term food literacy is emergent, and as a result the literature reflects a great variety of definitions. Simultaneously, new research and food literacy programming is being developed without an agreed upon definition of what food literacy is and how food skills, food security, and health literacy may fit with the definition. We undertook a scoping review and conceptual analysis to identify how the term is understood and to determine shared components of definitions. We found that although most definitions included a nutrition and food skills component, there was great variation in how the ability to access, process, and enjoy food was affected by our complex food system. We propose a definition of food literacy that includes the positive relationship built through social, cultural, and environmental experiences with food enabling people to make decisions that support health. We offer a framework that situates food literacy at the intersection between community food security and food skills, and we assert that behaviours and skills cannot be separated from their environmental or social context. The proposed definition and framework are intended to be guiding templates for academics and practitioners to position their work in education and advocacy, bringing together separate spheres for collective action.
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 | 0.042 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".