Policy Brief 3: Research and Innovation Supporting the Farm to Fork Strategy of the European Commission
Bibliographic record
Abstract
The EU Think Tank(as part of theFIT4FOOD2030 Coordination and Support Action) strongly supports the development of the Farm to Fork Strategy as a key component of the European Green Deal, recognising the need to transform the food system as a whole. This policy brief calls for innovative approaches tothe Farm to Fork Strategy to provide practical answersto two central questions: i) how can a shift towards healthier and more sustainable diets be facilitated?;and ii) how can all actors in the food system be empoweredto adopt more sustainable practices? Answers tothese questions raise the need fornew transdisciplinary, multi-actor and participatory Research and Innovation (R&I)approachesthat enable citizens,farmers, fishers, food processors, distributors, retailers and consumers to contribute to more coherent, cross policy-sector food initiatives that leverage on European food systems to deliver a balance of public goods (including food security and environmental integrity).
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 | 0.078 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.071 | 0.016 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".