The kingmaker’s dilemma: developing a conceptual framework for understanding coalition fragility in metropolitan governance
Bibliographic record
Abstract
The increasing incidence of fragmented electoral outcomes in metropolitan governments has elevated a recurring, under-theorised phenomenon: minority parties attaining kingmaker status in coalition arrangements. This paper introduces The Kingmaker’s Dilemma, a concept which captures the structural contradiction wherein a party with minimal electoral support acquires decisive influence over urban governance, often without assuming executive responsibility. Through a comparative case study methodology spanning Berlin, Nairobi, New York City, and Tshwane, the paper examines how these kingmakers obstruct or enable urban functionality from legislative positions, revealing patterns of power without accountability. The study also presents the Kingmaker Governance Matrix (KGM), a conceptual framework developed to guide future diagnostic and policy work in coalition cities. By embedding global insights within the realities of African metros, the paper argues that unresolved coalition dilemmas are fast becoming the principal source of instability in urban governance. It concludes with reform proposals to restore functionality and democratic legitimacy in city coalitions under pressure.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".