The rise of urban agriculture in Belgium: feeding the population through investing in two large cities: Brussels-Capital and Liège
Bibliographic record
Abstract
The loss of farms is problematic in Belgium, particularly in Wallonia (68% loss between 1980 and 2015) and in the Brussels-Capital Region and access to land is a real challenge for farmers to deal when setting up their farms. Recently, however, there has been a real increase in awareness of the importance of agricultural activity and land on the part of public authorities and especially of citizens who are trying to regain control of their food. These citizens are looking for a healthier and safer food in the face of the shortage of agricultural land in a densely populated country. The smallest interstices of the cities are occupied to set up innovative agricultural projects such as in Brussels and Liège where vegetable gardens and a Food Land Belt is developing to feed the populations of the cities. These agri-urban projects are the work of the citizens themselves, who take the initiative themselves to realize these innovative actions by pooling their knowledge and tools centered on new agricultural production models. We highlight the actions taken by certain segments of the population in the cities of Brussels and Liège where food, social and environmental issues have become a concern for consumers who invest in the green spaces left vacant compared to what is being done in other countries such as Canada or France, for more than a decade.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".