Barriers to building wildlife‐inclusive cities: Insights from the deliberations of urban ecologists, urban planners and landscape designers
Bibliographic record
Abstract
Abstract Cities are seen as quintessentially human; however, because they can offer viable habitat to many plants, animals and other forms of life, cities are also dynamic ecosystems. As urban areas expand to house more of the global human population and reduce natural habitat for wildlife, the need for wildlife‐inclusive urban planning and design becomes increasingly pressing. The 2019 Urban Wildlife Information Network Summit responded to this need by connecting a group of 80 scientists, urban planners and designers to examine the role of cities in combating the global biodiversity crisis. The Summit focused on identifying and addressing barriers to transdisciplinary work between these communities, such as disciplinary silos, varying incentive structures, funding, differences in spatio‐temporal scale, existing infrastructure and values and bias. We explore the challenges to network building for wildlife‐inclusive design and planning revealed by the Summit and offer potential solutions for overcoming these obstacles for more effective collaboration around wildlife‐inclusive cities. A free Plain Language Summary can be found within the Supporting Information of this article.
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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".