Clarifying, Creating, Capturing, and Catalyzing: An Exploration of Social Learning in the Evaluation of Community-scale Contributions to Sustainability Transitions
Bibliographic record
Abstract
Climate change, social inequality, ecological degradation, and political volatility are just some of the complex and interconnected challenges that underscore the need to accelerate sustainability transitions; long term, systems changes in the direction of sustainability. In an increasingly urbanized world, understanding how these challenges affect cities, and are addressed by urban actors, such as those representing municipal governments and civil society, is more important than ever. Of interest here is how social learning may shape the contributions of such efforts, particularly in spurring collective action at multiple scales. Yet the conceptual ambiguity surrounding social learning, for instance whether it is a process or outcome, and what roles agency and structure play in shaping learning pathways and outcomes, presents a barrier to understanding and leveraging it in the context of community-scale interventions. These gaps warrant the theoretical, methodological, and empirical work undertaken in this dissertation. Specifically, this dissertation clarifies social learning by applying a social practice lens, operationalizes it through a “multi-pronged, light-touch, utilization-focused” evaluation approach suitable to small scale interventions in the Greater Toronto and Hamilton Area (GTHA), Canada, and finally addresses the role of social learning in the contributions grassroots interventions make in advancing sustainability transitions.
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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 | 0.099 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".