Navigating the Path to Sustainable Cities: Exploring Institutionalization in Urban Sustainability Transformations
Bibliographic record
Abstract
People in cities are sowing ‘Seeds of Good Anthropocenes’ by experimenting with innovations that foster social and ecological sustainability and present hopeful solutions to persistent urban challenges. Such seed initiatives are often portrayed in theories of bottom-up, systemic change as the building blocks of urban sustainability transformations. This is because transformations towards sustainability in the world’s cities rely on our ability to innovate fundamentally new ways of thinking, living, and connecting with people and nature. Research exploring transformative change has led to much learning on the earlier phases of transformations and the actors involved in seeding change. Less is known about the later phases of transformations and the actors and interactions involved in institutionalizing change. Institutionalization, a process of embedding innovations into systems and structures, accelerates transformations and ensures that changes persist. In this thesis, I synthesized existing knowledge and advanced understanding about institutionalization as a transformational process. I conducted a case study exploring seed initiatives’ interactions with a local government policy to institutionalize urban agriculture in a borough of Montréal, Canada. I integrated semi-structured interviews (n=46) with governmental and non-governmental actors to participant observation and document analysis of institutional reports and policies. I thematically analyzed this data using an iterative approach that combined deductive and inductive coding to explore predetermined themes, while allowing emergent ideas to inform my process. The findings showed that, contrary to prevailing assumptions, local governments can catalyze transformative change in cities by intervening early to institutionalize promising innovations for sustainability. Interventions designed to mobilize collective action, reduce barriers to experimentation, and dedicate resources to support change in local communities can bolster the emergence of seed initiatives, while legitimizing and consolidating efforts to transform. This study revealed that bottom-up (informal) and top-down (formal) approaches combined contribute to achieving desired outcomes of institutionalization. This challenges the commonly held notion of inherent opposition between dominant institutions and seed initiatives and underscores the potential for synergistic partnerships between governments and seed initiatives to foment urban sustainability transformations. By shedding light on these dynamics, my research contributes to a deeper understanding of the interactions involved in the process of institutionalization, and the role they play in shaping transformational pathways
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.066 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".