Moving from features to functions: Bridging disciplinary understandings of urban environments to support healthy people and ecosystems
Bibliographic record
Abstract
Contact with nature can contribute to health and wellbeing, but knowledge gaps persist regarding the environmental characteristics that promote these benefits. Understanding and maximising these benefits is particularly important in urban areas, where opportunities for such contact is limited. At the same time, we are facing climate and ecological crises which require policy and practice to support ecosystem functioning. Policies are increasingly being oriented towards delivering benefits for people and nature simultaneously. However, different disciplinary understandings of environments and environmental quality present challenges to this agenda. This paper highlights key knowledge gaps concerning linkages between nature and health. It then describes two perspectives on environmental quality, based respectively in environmental sciences and social sciences. It argues that understanding the linkages between these perspectives is vital to enable urban environments to be planned, designed and managed for the benefit of both environmental functioning and human health. Finally, it identifies key challenges and priorities for integrating these different disciplinary perspectives. • Integrates perspectives from environmental, health and social sciences • Links environmental functioning, nature connectedness, and health/wellbeing outcomes • Identifies critical issues for planning, designing and managing urban environments • Sets out key challenges and priorities for a new interdisciplinary research agenda
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".