New Emerging Roles for Public Institutions and Civil Society in the Promotion of Sustainable Local Agro-Food Systems
Bibliographic record
Abstract
In recent years a growing engagement of local public institutions in the promotion of sustainable food production, consumption and distribution systems can be witnessed.On the one hand, the procurement of sustainable (local, organic, fair, etc.) food to institutional marketing channels such as schools, hospitals and company canteens has strongly gained importance.On the other hand, several cities and metropolitan regions have started addressing sustainable food as an integral policy issue, as is expressed in 'urban food strategies' and 'food charters' of cities like London, Amsterdam, Malmö, Rennes, Vancouver or New York.The emergence of urban food policies represents an important rupture with the past, when the governance of agro-food systems was principally seen as a matter of private market forces, in which local public institutions outside the agricultural and rural sector and civic movements had little role to play.Put in this perspective, these developments raise important questions on how to conceptually address and asses the relevance and (potential) impact of new urban and regional strategies in enhancing the transition to sustainable agro-food systems.Also important questions arise as to what are the most appropriate roles of state agencies, markets and civil society in the governance of sustainable agro-food systems at local and territorial level.This paper explores and aims to conceptually frame the fairly new, but rapidly spreading, phenomena of sustainable food procurement and urban food strategies.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".