How to make a city fall in love with an animal: Communication Strategies for Urban Wildlife Inclusion and, A Case Study on Opossums in Toronto, Ontatio.
Bibliographic record
Abstract
As humans continue to encroach upon wildlife habitats, conflicts between humans and wild animals have steadily increased. The current practices of urban wildlife management are ineffective, unsustainable, and have a demonstrated history of dangerous consequences. These practices operate from a deeply anthropocentric worldview which looks at nature as a resource, and animals as objects to regulate. The decisions taken under this mindset, have led to devastating consequences for the planet, the wildlife and humans themselves. \nThis paper employs an inclusive design philosophy, as an alternative and ecocentric approach, to urban wildlife management. It defines this practice of aiming for a peaceful co-existence as urban wildlife inclusion. It argues that wildlife management should no longer cater only to humans, but it must consider the agency and autonomy of animals and treat them as equal stakeholders of the planet. \nAlong with that, it also makes a case for expanding the sphere of inclusive design to include urban wildlife in it, effectively creating a new area of inclusive design research that expands the system of inclusion beyond humans. \nThe paper then goes on to propose an urban wildlife inclusion framework and a communication model, which will assist in designing communication strategies for gaining and sustaining community participation for urban wildlife. This is followed by a documentation of an on-going project which utilizes the proposed framework to raise social awareness about Virginia opossum population in Toronto, Ontario. During this documentation, it also discusses the concept of “co-designing with animals”. \nThe paper concludes that there is a need to steer ourselves towards social inclusion of wildlife and ecocentrism. It also hopes that the framework and model designed during the course of this research can act as starting points for building further models of inclusion.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".