Small wins in practice: Learnings from 16 European initiatives working towards the transformation of urban food systems
Bibliographic record
Abstract
In this study, we examine how 16 initiatives across Europe are addressing 'wicked' food system issues by mobilising local networks and implementing small-scale but impactful changes in urban and peri-urban regions. To map the potential of these initiatives to contribute to large-scale change, we apply the Small Wins Framework proposed by Termeer & Dewulf (2019). By analysing data collected through interviews with participants working on initiatives spanning 13 cities across 9 European countries, we identify the manifestation of six propelling mechanisms that signal the capacity of small wins to bring about systemic change. Findings from this study reveal the presence of most mechanisms across the included initiatives. However, the ways in which these mechanisms appear depend on various factors such as stakeholder motivation, the maturity of the initiative, the need for additional funding, local food culture, and the regional and national political landscape among others. Our analysis indicates that the Small Wins Framework could be successfully used as a mapping tool in urban transformation processes, but it is likely to be more effective as a tool for reflexive monitoring rather than ex-post evaluation. Drawing on the impacts of various large-scale disruptions on the initiatives, we suggest that social, political, and economic shocks can present windows of opportunity to accelerate change and that initiatives performing well under such pressure should be supported in their pursuit of systems transformation. Lastly, we recommend non-linear growth strategies such as spreading, deepening, and expanding, as ways to compound the impact of small wins.
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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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".