Applying Mixed Methodologies to Inform Urban Conservation: Policy, Knowledge and Behaviour at the Interface of Nature and Society
Bibliographic record
Abstract
Globally biodiversity is in decline and the human population is urbanizing. The loss of species is so great, it has been dubbed the “sixth mass extinction.” Over half of the global population now live in cities. There is the loss of biodiversity coupled with the loss of experience of nature in our daily lives. Interacting with nature has been linked to improved health and well-being. Despite the co-benefits for both people and nature, there is an implementation gap between the science, policy and practice. My dissertation applied the concept of scale from spatial (landscape) ecology to an interdisciplinary context: peoples’ values of nature.\n\nAt a local scale, I explored peoples’ emotions towards urban greenspaces in a large Canadian city, during a time of abrupt change and societal shock – the COVID-19 pandemic. Parks acted as an emotional buffer, as places of escape and recovery. Parks as a support to well-being can be leveraged and translated into political capital for park maintenance and for park and greenspace expansion in large urban centers. At a national scale, my coauthors and I investigated Canadians’ values towards native bees and perceived barriers towards their conservation. Canadians value native bees for their contribution to people and want the federal and provincial governments to take the lead in their conservation. This grassroots support for conservation should be communicated to decision-makers. At the global scale, I analyzed publications from two environmental organizations to study how the conversation about sustainability has changed over the past 25 years. Funding shapes sustainability communication. Expectations and priorities of donors can hinder capitalization on known science. Making knowledge accessible and relevant to funders informs sustainability practice. Collectively, these results provide insights into biodiversity conservation in urban contexts and sustainability practice.
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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 | 0.137 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".