Urban agriculture in northern and southern countries: A conceptualization of dynamics and challenges
Bibliographic record
Abstract
According to the United Nations Organization, the world's population is expected to increase by more than 3 billion by 2050. This increase will take place directly on the space adjacent to the cities, A peri-urban area where agricultural activity is often dominated. Although the city and its neighboring countryside have always maintained good neighborly relations, it should be remembered that the horticultural belt has long been a source of nourishment for its city and that, as different researchers have pointed out, the master market gardener has played a fundamental role between the city and the countryside. The market gardener was "rural by his work" and "urban by his habitat and his corporate organization". Today, it is incontestable that this configuration has been upset. In the countries of the North as well as in the South, the inexorable progress of the city has nibbled away at the agricultural areas that are constantly receding. This has led to the loss of 32 million hectares over the period 2000/2001 to 2010/2011, according to a report of the Food and Agriculture Organization of the United Nations. Moreover, new relationships are born in these so-called peri-urban spaces, where new functions have been added to that which until recently dominated, i.e. agricultural production. Some researchers speak of "agriculture in the city's countryside" or of "urban countrysides". Indeed, the city and its various functions take root in the countryside and the simple dichotomy between rural and urban areas has tended to disappear around cities of the North, where peri-urbanization continues its work, and in the countries of the south it has accelerated. Agriculture thus has been regaining its original function of "nourishing its city", thus creating ties that had become somewhat reduced. In the countries of the North and the South, many initiatives are emerging in this urban fringe, where each segment of the population tries to take maximum advantage of the opportunities that can arise there. In relation to this, the problem is how the new modes of production of this agriculture (or rather of these agricultures) are apprehended in the countries of the North and of the South. Because of the numerous crises in the agricultural world in northern countries, today more than ever urban dwellers are more likely to want a healthy diet and to know the origins known. Examples abound in northern countries such as France, Canada, Switzerland (Food Guilds), and Belgium with its Solidarity Purchasing Groups for Peasant Agriculture. The challenges and issues associated with all these trends are the focus of this presentation.
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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 | 0.001 | 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".