Development of the Food Systems Literacy Competencies Framework for youth: A modified Delphi study with experts
Bibliographic record
Abstract
Food systems have changed drastically in the last 50 years, are continuously globalizing, and are constantly responding to environmental, social, economic and political challenges. While a primary goal of food systems is to provide food security, food insecurity rates have continued to rise in recent years. To deal with these systemic problems, food systems experts call for food systems literacy. However, there are no common frameworks outlining what people should know about food systems, making it challenging to relay information into education and to the general population. As such, the aim of this research was to identify competencies for food systems literacy for youth in Canada. We conducted a 2-round modified Delphi study to achieve consensus on a list of key competencies for food systems literacy. Delphi studies allow for broad consultation of expert judgements to achieve consensus on complex issues. This resulted in a list of 50 key competencies for food systems literacy (out of 131 tested) across themes for Indigenous food systems, food systems activities (i.e., production to waste as well as some overarching and technology themes), sustainability, food security and governance. This Framework of competencies is the first of its kind and will be useful for directing how food systems can be integrated into education as well as be a guide for food systems literacy evaluation tools.
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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 | 0.049 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".