Sustainability-Enhancing Initiatives in the Ontario Greenbelt: Evaluation of Three Exemplary Cases
Bibliographic record
Abstract
The overwhelmingly complex challenge of intensifying environmental issues and rising social \ninequities at all scales, community to global, and the lack of impactful policy and action, contributes \nto the negative narrative of the environmental and sustainability field. The severity of the crisis \ndeserves to be critically analyzed; however, the negative narrative does little to encourage genuine \nengagement and motivation for progress. Therefore, the objective of this master’s research paper \n(MRP) is to explore stories of positive, creative sustainability initiatives to illuminate examples of \nsustainability success and how lessons from these stories can inform sustainability practice. This \nMRP examines three case studies, selected through developed criteria, and explored through a \nconstructed conceptual framework informed by core sustainability concepts in the literature to \naccount for complexity, interconnectivity, and depth. The Ontario Greenbelt, the focal system, \nprovides a specific region that is sustainability-minded due to the protection of land, associated \nagricultural landscape, and structural support from sustainability organizations, including the \nGreenbelt Foundation. Three case studies – the Greenbelt Farmers Market Network, the Alderville \nBlack Oak Savanna and the Shared Path Consultation Initiative – were selected through application of \nexplicit sustainability-based criteria and examined through a conceptual framework lens informed by \ncore sustainability concepts in the literature. Each one is centered on a different dimension of \nsustainability. Together they reflect the complexity of the Greenbelt as a social-ecological system. \nThe reporting uses a storytelling approach, informed through peer reviewed and grey literature, \navailable documentation about initiative activities and interviews with organizers of the initiatives. \nEach case study provides consistent insights into practices that enhance sustainability, including \nunderstanding and appreciating complexity and interconnectivity, supporting community capacity, \nnetworking, and forming respectful relationships, and ensuring equity. Unique lessons from each \ninitiative were also observed, providing further insight into sustainability thinking and practices given \nthe social-ecological context of the respective initiative. These stories illustrate the value of focusing \non how sustainability is actively being enhanced within communities and the importance of support \nsystems like Greenbelt to encouraging sustainability. Implications on the broader literature includes \napplications of the framework in examining project in other social-ecological contexts, applying \npractices in other communities or larger scales, and encouraging research focused on positive \npathways to progress towards greater sustainability
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".