Food literacy: An international update on its conceptualisation, measurement and application
Bibliographic record
Abstract
Since 2010, the term food literacy has increasingly become part of the community and public health nutrition vocabulary of practitioners, policy makers and researchers. The only empirically derived definition of the term was developed in Australia through a study of Australian food experts, review of Australian interventions and a study of young people in Brisbane across a spectrum of disadvantage (Vidgen 2014). This work included the identification of eleven components organised into four domains of planning and management, selection, preparation and eating, a conceptualisation of how food literacy related to nutrition and more broadly where it may fit within broader public health programs. Since this time, this work has been widely cited in the international literature as various countries progress their conceptualisations of food literacy and what it means for their policy and practice. This presentation will: • provide an overview of recent systematic reviews of definitions and conceptualisations of food literacy from different countries. • provide a summary of current efforts to develop measures of food literacy, with a particular focus on Italian, Dutch and Canadian tools. • present brief case studies of food literacy work including the United Nations Food and Agriculture Organisation’s Scaling Up Food and Nutrition Education in Schools as part of the Decade of Action on Nutrition and Canadian and Italian work to train a nutritionists to develop food literacy among their citizens. This presentation aims to contribute to nutritionists’ understanding of the field of food literacy to support them in evidence based practice. Building understanding of food insecurity for com
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".