Let it grow wild! A more-than-One-Health perspective for wild spaces in cities
Bibliographic record
Abstract
Urbanisation disrupts connections with nature, compromising human, biodiversity, and environmental health. Urban Wild Spaces – pockets of unmanaged and spontaneous vegetation within cities – may offer an alternative way of promoting wellbeing while providing opportunities to support biodiversity and nature connectedness. This conceptual paper explores the opportunities and challenges of integrating urban wild spaces by proposing a more-than-One-Health perspective. This approach integrates One Health and more-than-human approaches to emphasise the linkages and relationalities of human and non-human health within urban wild contexts. Through this perspective, we examine five global mini-case studies that demonstrate diverse approaches to health within urban wild spaces: vacant lands in Santiago de Chile, urban forests in Edmonton, urban wildness in Singapore, peri-urban forests in Bogotá, and post-industrial landscapes in Copenhagen. They reveal how these spaces can foster health connections among people, biodiversity, and the environment. Our analysis suggests that urban wild spaces represent opportunities for multispecies flourishing, promoting coexistence and offering a deeper understanding of health from sensory, embodied, ecological and health restorative perspectives. Finally, we propose planning, design, and integration strategies that highlight the importance of these spaces for urban biodiversity and underscore their potential as restorative, therapeutic, and multi-sensorial environments that enhance multispecies wellbeing within urban landscapes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".