The Significance of Leadership in the Evolution of Policy Styles: Reconciling Policy-Making in the Short and Long Term
Bibliographic record
Abstract
Abstract Can relatively short-term political leadership affect long-term national policy styles, and if so, how? We examine these questions in order to explain the temporal dynamics in national policy styles observed in several recent studies whose findings have challenged the traditional view that policy styles lend continuity and stability to policy-making. In its original formulation, the concept of policy styles refers to the extent to which governments are committed to anticipatory or reactive policy-making, as well as to whether they seek to reach consensus with societal groups over policies or simply impose decisions on them. Following these definitions, we contend that political leaders can alter a long-held policy style and often do so due to strategic considerations, either in an attempt to improve their policy performance or in response to an exogenous shock, such as an economic crisis. We illustrate this argument by using longitudinal records of changes in country-level governance indicators which include data on dimensions of policy styles. Specifically we examine how two of the Sustainable Governance Indicators compiled by the Bertelsmann Foundation changed for Ireland, Italy, Malta, and Poland between 2014 and 2020. As the cases show, government leaders sought to alter long-standing processes and the arrangements of governance and policy-making. Our empirical findings support the overarching argument that the study of national policy styles should be combined with that of political leadership.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".