Agro-ecological urban agriculture and food resilience: The Case of Quito, Ecuador
Bibliographic record
Abstract
The rural and urban divide, promoted by capitalism first and global neoliberalism later, has characterized the countryside as synonymous with “backwardness” and established the city as a model for “progress. ” In recent years, promoting agriculture in cities seemed counterintuitive. Nevertheless, during the last decades, agricultural practices in the urban realm have been encouraged, and with great effort, by a group of cities worldwide. Quito is one of them. The Participatory Urban Agriculture Project (AGRUPAR) has promoted and supported urban agriculture in Quito for almost 20 years. However, aware that the food situation of its population requires stronger efforts, the city has decided to go beyond urban agricultural production. Led by AGRUPAR, and together with other municipal actors, such as the Metropolitan Directorate of Resilience, and the Secretariat of Productive Development, the Municipality of the Metropolitan District of Quito (MDMQ) is implementing public food policies that have outlined, as one of their central objectives, the need to strengthen the city's food security and food resilience. This text presents a brief history of urban agriculture in Quito and reviews some of the achievements of AGRUPAR. Based on this experience, the authors hypothesize that cities that have gone from promoting urban orchards to establishing urban agricultural programs are in a better position to implement food policies as a contribution to resilience and sustainable urban development. This article displays the importance of clearly understanding the food value chain and the set of strategic dimensions that currently shape the agri-food system. The aim is to better connect the production, processing and transformation, distribution, sale and storage, commercialization, consumption, and post-consumption with the right to food, the right to the city, and a healthy environment to achieve food security. Although the results achieved thus far are valuable, if the benefits of urban agriculture are to contribute to improving Quito's food security and resilience, additional progress is necessary. Therefore, it is imperative that a proposal be presented which includes urban agriculture as part of a city-scale urban policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".