Transdisciplinary Partnerships for Food Literacy Education Research and Professional Development
Bibliographic record
Abstract
Food literacy education is directly linked to global issues, such as social inequity in food access, environmental degradation, and economic imbalances in trade and corporate consolidation. While food literacy education fosters informed consumers who prioritize sustainability and ethics in their food choices, while also empowering them to engage in community and political action for transforming food systems, it currently is over-theorised and under-practiced. The Food Literacy International Partnership (FLIP) is a project that brings together scholars from four countries – Australia, Canada, Sweden and the US - to create partnerships that are focused on food literacy practices and outcomes, and on supporting educators. In this paper, we describe the development of these transnational, transdisciplinary partnerships, noting the affordances and challenges that emerged and how these were addressed. As part of implementing these partnerships, we published a website that hosted resources, held webinars, worked directly with educators, supported junior scholars, convened a symposium, and presented at conferences; through all of these, we added individuals and groups to our partnership network. The development of these partnerships highlights the need and potential for more discourse and collaboration around food literacy across national borders, enabling this work to have the most impact in contributions to more sustainable and socially just food systems worldwide.
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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 | 0.063 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.057 | 0.007 |
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".