Making reform stick: Political acumen as an element of political capacity for policy change and innovation
Bibliographic record
Abstract
Abstract Political acumen as an element of policy capacity involves feasibly and successfully steering policies through organizations and systems. “Normal” policy-making makes no great demands in this regard, and so this paper focuses instead on deep policy reforms that typically engender resistance among organizations and stakeholders. Our approach assumes that the nature of these types of changes is paradigmatic and non-Pareto optimal (imposing losses), and take place within policy systems having reasonable degrees of feedback that require policy reformers to negotiate and adjust their reform agenda. We offer a model of political acumen in dealing with deep policy reform that has 10 characteristics, collected under three sub-categories: (1) the nature of the policy problem, (2) the policy response, and (3) policy skills or capacity. Based on this model, we assess the advice on policy reform in the literature and from the OECD and the World Bank — organizations both deeply engaged in governance reform agendas. Basic tools for policy managers are compensating losers, spreading losses over time, grand parenting, and insulating decision-makers, while elected leaders need to develop mandates for change, build coalitions, and engage in heresthetics. At the highest level, political acumen involves the strategic capacity to manage and implement significant policy change.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".