COLLECTIVELY GARDENING THE URBAN PUBLIC SPACE IN MEXICO CITY: When Informal Practices Interact with the State
Bibliographic record
Abstract
Abstract In recent years, a growing number of citizen‐led gardens have appeared in the urban public spaces of large cities across the world. While many of these projects are initially launched informally without any support from the state, they gradually become integrated into the social fabric of the city. To understand the evolution of the formal–informal boundaries of the practice, we argue that we should be paying attention to the specific institutional contexts that frame gardeners’ interactions with public authorities. Drawing from a study of citizen‐led gardens in Mexico City, we show that informal urban gardening becomes a disconnected‐from‐the‐state practice. On the one hand, the Mexico City government has shown a growing interest in regulating urban agriculture. On the other hand, gardeners are increasingly trying to find their own ways to formalize and perennate their practice. We suggest that this disconnection between gardeners and the state is best explained by the weakness of the institutional context in which their interactions take place. A top‐down policymaking process, along with the incapacity and unwillingness of the multi‐leveled city government to implement policies effectively, reinforces norms of mistrust and generates low expectations among gardeners as they interact with local authorities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".