Food Policy Councils as strategic catalyst for innovation in city- region governance and planning
Bibliographic record
Abstract
During the last decade an increasing awareness regard the 'new food equation' (Morgan and Sonnino 2010) – and the very tangible challenges that entails – started growing significantly among different governments in the Global North. In only few years time a unique 'wave' of worldwide institutional attention has arisen and a new generation of urban and regional planning tools – the 'food strategies' – was born (the Toronto, London, Amsterdam and New York ones represent only a small part of them). Nevertheless, in order to make 'food system thinking' operable on governance level and turn visions into reality, a high degree of 'creativity' (Kunzmann 2004) and innovation was being required from local administrations. Perhaps one of the most compelling answers that stemmed from this pressing demand is represented by the so-called 'food policy councils' (FPCs). As Mark Winne observes: "Though lacking authority and respectable budgets, these entities have become de facto food system planning agencies" (Winne 2004: 15). \nThe present paper will advance the hypothesis that FPCs (and alike organisations) in many occasions have constituted the strategic catalyst that made it possible for 'food planning' visions to 'travel' through different "institutional sites in a governance landscape, penetrate governance processes and sediment into governance cultures" (Healey 2007: 23). In particular, by taking advantage of relevant examples from both North-American and Western-European cities, the influence of three distinct aspects over such transformative dynamics will be examined. A first area of investigation will focus on how conditions like FPCs 'location' and 'degree of integration into a city government' (Dahlberg 1994) increase or weaken their credibility and 'persuasive' capacity. Such conditions will also be examined with relation to the necessary factors for delivering more 'electoral cycle'-proof strategies. The paper will then explore how the different strategies' predominant connotations (health, labour, 'zero carbon' growth, poverty, etc.) reflect different city departments 'sensitizing' and, accordingly, diverse food system governance 'capacities' (Mendes 2008) development. Finally, in the third part, will be addressed the relevance of 'scale' and how the 'food planning' inherent complexity is fostering the gradual engagement of multiple governance levels (e.g. the city of Boston, having already a city FPC, has asked for the establishment of a state-wide one, while New York City is doing vice-versa). The paper will conclude rising some key questions on the relationship between cities' size and the opportunities for the sustainable food paradigm to transform their 'mainstream' planning processes and cultures (Healey 2004).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".