Systemic Management Practices—Enabling Local Governments to Adapt in Response to Complexity
Bibliographic record
Abstract
Local governments are increasingly navigating accelerating change and escalating complexity caused by interconnected crises, commonly referred to as a global polycrisis. These crises, including climate change, lack of affordable housing, declining mental health, and geopolitical instability, both shape and are shaped by local conditions. Cities face growing pressure to equitably provide services that are responsive to evolving community needs while contending with the systemic nature of contemporary challenges. However, local governments are often constrained by conventional management frameworks and practices that do not match the complexity of today’s challenges. The purpose of this conceptual paper is to explore how systems science can be leveraged to define and characterize a transformative new type of management designed to enable local governments to more adequately address emerging complexity. To this end, the authors review the literature on contemporary management practice and explore how management for local government can be reframed in alignment with the insights from systems science, using a service ecosystem lens. The findings point to a needed shift toward systemic management practices that are integrative, collective, and adaptive. The authors illustrate the practical relevance of these three characteristics and conclude with recommendations for research, policy, and practice aimed at building the institutional capabilities required to transition toward systemic management frameworks and practices that match the complexity of the polycrisis.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".