Streets as new places to bring together both humans and plants: examples from Paris and Montpellier (France)
Bibliographic record
Abstract
Greening public city space is a growing issue in France. With examples drawn from Paris and Montpellier, this article seeks to understand what happens when city-dwellers green the public space outside their door and when policies encourage spontaneous flora on the street. Plants were already part of ancient cities and have been a tool for urban planning since the nineteenth century leading to the development of public green spaces and street-tree planting. Urban ecology sparked an interest for spontaneous flora in the 1980s. Public policies concerning water, climate, and biodiversity have been trying to take this unbidden vegetation into consideration since the beginning of this century. Besides, the social sciences have shown that city-dwellers are interested in plants to embellish their balcony, and in city gardens and parks. We tried to find out if this vegetation can be more than just a tool to plan, to green, to bring biodiversity, and to beautify urban space. We argue that letting planted and unbidden flora colonize sidewalks and allowing people to act directly on it brings residents and plants to co-inhabit and co-domesticate the streets, and challenges the timelessness of a city by introducing a life cycle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".