Monitoring sustainability of urban agriculture: Who is going to do it and how?
Bibliographic record
Abstract
• Consistent evidence is needed of sustainability impacts of urban agriculture. • Proper monitoring requires a comprehensive set of sustainability indicators. • Standardisation is needed to compare over time and space. • Cooperation with interest groups or educational institutions can give valuable data. • Convenient tools for data collection and storage are essential. Urban agriculture is often considered a tool to increase the economic, social and environmental sustainability of cities and city food systems. However, sustainability is difficult to measure, resulting in debate about how sustainable urban agriculture truly is. There is therefore a lack of incentive to promote urban agriculture or protect existing initiatives that are threatened by development pressure on urban land. Monitoring the sustainability impact of urban agriculture could provide evidence and enable politicians and decision makers to make informed decisions about whether and where to prioritise different forms of urban agriculture above competing interests. We used case examples from five European cities to identify the challenges involved in monitoring urban agriculture, from selecting indicators and gathering data, to using the results. We found large differences in approach in terms of what topics to monitor and who was responsible, who gathered the data and when, what data was recorded and how they were stored, and how findings were disseminated or published. Based on these experiences, we recommend stronger involvement of existing interest groups and educational institutions in monitoring urban agriculture, and promotion of convenient tools for data collection by citizen science and for long-term data storage.
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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 | 0.001 |
| 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".