Embracing Coexistence: urban design strategies for creating wildlife-friendly cities.
Bibliographic record
Abstract
Cities are especially important for human development; it is where people live, build communities, work, learn, and thrive. In cities, human and technological development coincide. But, while building urban and increasingly dense spaces for people’s comfort, other beings are in effect forced to adapt to the ever-growing and changing human habitats. Additionally, major challenges such as climate change resulting in wildfires, flooding, and extreme weather conditions are forcing wild animals and invertebrates to shift closer, if not entirely into human occupied areas. The intense urban densification of the world has affected non-human beings that used to roam freely in those unoccupied areas. Some animals are permanently displaced, while others have successfully adapted to the new human centric environment. Even though wild animals have been able to adapt, cities have not been designed for them, which complicates the way in which they can survive, even as they evolve to co-exist with human urbanites. With an inclusive design approach, this project recognizes both the differences and similarities between human inhabitants and ‘urbanized’ wild animals, as part of having a better understanding to improve the coexistence of species within an urban context. It examines and outlines a range of contemporary initiatives that have been developed and proposed with a focus on designing for animals. The project also gathers information from interviews with experts in wild animal welfare, animal ethics, and sustainable urban planning. In addition to the interviews, data is also collected from an anonymous survey open to people that lives in Toronto. As a result of the research, initial guidelines are proposed to either design new cities or adapt existing urban centres with a more inclusive strategy. These guidelines are organized in three principal areas: infrastructure and planning, government policies and community involvement, and biodiversity conservation and animal welfare. While the project concludes with proposed guidelines, it is understood that it is just an initial stage, and that the journey to have animals, nature, and human beings successfully co-exist in urban centres is an ongoing project in constant adaptation to the rapid and significant changes we are seeing in the environment today.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".