Bibliographic record
Abstract
The expression “urban agriculture” refers to the emergence in many cities of areas cultivated by farmers who distribute the fruits of the land they work in the environs of the zone of production. The movement, born in response to a range of real needs, has become a global phenomenon, and has taken on an organized form in a large number of cities: from Mumbai to Beijing, London, New York, Detroit, São Paulo, Rosario, Vancouver, Tokyo, San Francisco, etc. The urban farming movement, with its production of food, its educational aims and the idea of creating sustainable situations, has been able to take root in many cities and metropolises as it is closely integrated with the urban ecosystem. For the most part it is the poor and women who, working on small farms located both inside and outside the city, are nurturing this politico-cultural movement. Their agricultural settings are creating new and interesting landscapes that need to be analyzed from an aesthetic perspective as well for the influence that they might have on contemporary landscape architecture. The phenomenon could have repercussions on the visual conventions of the urban and suburban environment and even affect the behavior and lifestyles of city dwellers should it develop on a larger scale.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".