Diverging Stories on food system transitions. A qualitative analysis of policy narratives in the public consultation on the European Commission’s Farm to Fork Strategy
Bibliographic record
Abstract
The transition towards more just and sustainable food systems is a major challenge which requires persuasive policy narratives for motivation and coordination. However, research about how relevant European actor groups view crucial elements of food system transitions is scarce. The consultation on the European Commission's Farm-to-Fork Strategy provided an opportunity to analyze how elements of food system transitions are represented and connected in the policy narratives of European stakeholders. A deductive-inductive content analysis of 164 public responses, using an analytical framework based on the multi-level-perspective of food system transitions, found twelve distinct narratives, mostly focused on agricultural production. Ten of them showed clear connections to three established agricultural policy discourses: neomercantilism, multi-functionalism and market liberalism. While the narratives together provided a comprehensive account of transition drivers, challenges, opportunities and responses, individually they presented rather partial perspectives. For example, market-liberal narratives emphasized the role of value chain actors and “consumer choice”, whereas multifunctionalist narratives called for more regulation. Farmers' associations mainly deployed neomercantilist, NGOs multi-functional and industry representatives market-liberal narratives, revealing dissent on the importance and desirability of change. The findings indicate that shifting the discussion venue away from core agriculture policy arenas broadened entrenched discourses only marginally. • Analyses public responses to the EU's Farm to Fork Strategy. • Identifies 12 narratives with strong links to established, competing CAP discourses. • Shows that actor groups frame elements of food system transitions differently. • Provides insights into ongoing conflicts over the implementation of the F2F strategy.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".