Breaking the Walls of Complex Systems Change in Cities: A Service Ecosystems and Psychological Perspective
Bibliographic record
Abstract
To meet the targets of the Paris Climate Agreement, municipalities must facilitate transformational change at a local level. From a city perspective, the climate crisis intersects with many other complex challenges; therefore, transformational change should be coordinated in a purposeful and holistic way for it to address multiple challenges effectively, and to improve the lives of all citizens. This necessitates a change in the mindsets of municipal leaders, along with a systemic way of approaching strategic management. This article leverages an interdisciplinary lens based on social systems theory, combining management, science, and psychology to derive conclusions for transformative action. The authors draw from their experience facilitating change within municipalities to illustrate key points. This article derives recommendations for policymakers and research recommendations based on the view of the city as a complex system. At an organizational level, cities need to develop strategies that represent the diversity of its citizens and integrates localized social, environmental, and economic goals. At an individual level, city leaders and staff need to develop three kinds of knowledge: system knowledge, transformation knowledge, and action-guiding visions. The diversity and complexity of challenges that must be overcome for cities to become sustainable, just, and resilient requires a shift in the mindsets of city leaders and other stakeholders as well as the transformation of strategic management practices. Currently, there is a lack of accessible and practical evidence-based solutions available to municipal leaders to support facilitating this change. The authors call for research and clear recommendations on how to work toward closing this gap.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| 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".