Characterizing non-governmental organizations and local government collaborations in urban forest management across Canada
Bibliographic record
Abstract
<title>Abstract</title> Urban forests are being threatened by rapid urbanization, biodiversity crises, and climate variability. In response, governments are increasingly collaborating with the public for solutions to these mounting challenges. Non-governmental organizations (NGOs) are dominant players in these collaborations because of their ability to deliver on communities’ environmental issues. Despite their growing visibility in forest management, there is a lack of attention directed to the forms of NGO relationships and their range of collaborative activities. This study focuses on addressing these gaps and examining collaborations between local governments and NGOs in urban forest programming by characterizing their components including mandates, relationship ties, accountability, resource exchange, and power dynamics. We collected data using semi-structured interviews with three groups: leaders of NGOs, municipal government officials in an urban forest or public works departments, and urban-forest experts who have observed their interactions. The participants represent 32 individuals in nine Canadian cities. Our results indicate that NGO-government collaborations have relational ties and accountability processes that are both formal and informal in nature; however, formality in collaborations is associated with the amount of funding, proximity to government, or size of the NGO. Additionally, our findings suggest that NGOs present an opportunity to local governments to supplement their resources and capacity. While the strength and formality of collaborations may be a product of NGO size and budgets, public servants should hesitate to engage smaller, grassroots NGOs to realize their public service mandates. Characterizing the components of these governance processes provides a benchmark for practitioners participating in similar public-civic interactions and arms them with the knowledge to navigate collaborative decision-making.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".