Local environmental stewardship and social-ecological bright spots in New York City
Bibliographic record
Abstract
Due to a wide range of benefits, some of which are highly visible, urban vegetation, including tree canopy, lawns, gardens, vacant lots, and urban agriculture, has become an important focus for urban sustainability planning. As many major cities invest large amounts of public funds into programs to increase urban vegetation cover, city planners require scientific understanding to help them determine effective and equitable greening strategies. Because cities are complex social-ecological systems, with a range of ecological, socioeconomic, and technological factors driving vegetation dynamics, developing this understanding will require new, multi-disciplinary thinking to understand the many drivers of urban greening and the emergent interactions between them. In particular, in human dominated ecosystems such as cities, human visions, values, and the social relations that shape urban forests need to be incorporated into assessments of urban vegetation.In this thesis, I examine the impacts of local environmental stewardship groups, an important part of environmental governance in many major US cities, on vegetation change and management in New York City.In Chapter 1, I review the development of the field of urban ecology, outline the body of literature on environmental governance with a specific focus on local environmental stewardship, and discuss applications for urban vegetation modelling and management. In this review, I develop a framework that can be used to empirically assess the multiple drivers of urban vegetation change; integrate metrics of stewardship into urban vegetation modelling; and learn from examples of stewardship success to identify best practices for stewardship group organizing.In Chapter 2, I examine the relationship between the presence of neighborhood stewardship groups and vegetative change in New York City between 2008-2016. Using a combination of remote sensing methods and linear mixed effects models, I estimate the statistical effect of stewardship presence on neighborhood-scale ecological change across the entire city. I found that the number of stewardship groups present in a neighborhood has a significant, positive relationship with decade-scale vegetative change in New York City.In Chapter 3, I investigate bright spots of stewardship practice, neighborhoods with much better ecological outcomes than expected, by examining the enablers and barriers to capacity building that shape effective stewardship action. To amplify the impact of effective stewardship actions, we must first understand the capacities that enable them and how capacity building can be best supported. Using a mixed methods approach combining modelling, interviews, and qualitative analysis, I examine three assets that contribute to stewardship group capacity. I show that stewards believe that their most effective actions are nurtured through the human-to-human relationships built with volunteers, policymakers, and communities, and that they are hindered through lack of access to knowledge, agency, and funding.In Chapter 4, I investigate which characteristics of stewardship are generalizable and which are tied to specific local contexts through a comparison of the capacity building processes of stewardship groups in urban New York City and suburban Greater Montreal. Using qualitative content analysis, comparing results from interviews in both systems, I found that stewardship groups in each context resemble each other, but work within vastly different contexts and ultimately, via vastly different processes. I hypothesize that key differences between stewardship communities can be further understood by the mediation of demographic contexts present.Overall, I show that local environmental stewardship groups play an important role in urban vegetation change in New York City and highlight the importance of incorporating the many ways of understanding stewardship in managing complex social-ecological systems
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".