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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".