FOODLIT-PRO: Developing Food Literacy
Bibliographic record
Abstract
At the National Action Plan for Food and Nutrition 2015-2020, the WHO highlights that poor dietary habits are responsible for many non-communicable diseases (e.g., diabetes, cardiovascular diseases, some cancers).Given the urgency to improve food intake, the lack of consensus over the concept of food literacy and the need of research in this domain, compromises the improvement of eating habits.To identify theoretical gaps, two conceptual models of food literacy (FL) are confronted and goals to develop FL are presented (construct, measure and intervention development) in the ambit of the project FOODLIT-PRO.The first model defines FL as intertwined food-related knowledge, competencies and behaviours that promote physical and psychological wellbeing, having as domains Planning, Selecting, Preparing, and Eating.The second model characterises FL as a combined set of food-related skills and knowledge that support a daily healthy diet, building resilience and incorporating the domains of Preparation, Organisations, Psycho-social Factors, and Knowledge.The lack of psycho-social variables in the first FL model, which is achieved on the second one, highlights the relevance on research concerning psychological dimensions of FL.Aiming the development of this field, this work presents the protocol for the first stage of FOODLIT-PRO.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.012 |
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".