Reconceptualizing the Politics-Administration Dichotomy to Better Understand Public Leadership in the Twenty-First Century: A Multilateral Actors Model
Bibliographic record
Abstract
The long-standing discussion of the politics-administration dichotomy is as relevant in contemporary public administration as at any time in the past. The significant changing context and persistence of the discussion on the practice of the politics-administration dichotomy calls for addressing what Overeem observes as the need to better describe the highly complex relations between politicians and administrators. Two implicit assumptions drive the continued relevance of the discussion of political-administrative interactions. First, the discussion matters because the actors in question fulfill various public leadership roles. Second, the political-administrative dialogue matters to questions of constitutionalism, values of representative government, and facilitating institutions of democracy. In this article, we offer a model of public managers’ engagement in the “how” with a framework that explicitly outlines the range of relationships. We draw on empirical research to outline the current reality of at least five significant types of actors, often working collaboratively in multilateral relationships. The first section of the article discusses the genesis and implications of the politics-administration dichotomy. It then proceeds to establish its persistence over time, followed by an explanation of the logic of our approach. Next, the discussion shifts to the analytic advantage of a continuum model across five categories of actors within the governance process, each possessing the potential for public leadership, showing how this model illustrates paths for addressing the problem we have identified. Finally, we discuss the implications of our proposed model for future research design and practice.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".