Paradigmatic stability, ideational robustness, and policy persistence: exploring the impact of policy ideas on policy-making
Bibliographic record
Abstract
Abstract In the policy world, the idea of ideational robustness deals with why and how policy elements can be maintained over time and the implications this has for government strategy and activity. Past approaches to ideas and their influence on public policy stressed the disparate roles of multiple policy mechanisms such as path dependency or the nature of policy networks in either driving policy change forward or ensuring persistence of a policy. Ideational robustness on the other hand allows for the possibility of some changes occurring in policy environments and components while motivating cognitive and normative policy ideas are adapted and retained. While ideational persistence has often been identified as a source of policy stability, the possibility that some ideas allow lesser or greater levels of change in policy components while basic policy content remains more or less intact (ideational robustness) is much less well understood. The article reviews the literature on policy change and stability which highlights the influence of different policy processes on overall policy dynamics and the role played by policy learning in promoting ideational robustness. Using evidence from military doctrines and procurement in Canada and Australia, the study finds that the two countries drew different defence policy lessons from the end of the Cold War which contributed heavily to the extent of robustness of their defence policy ideas. The case studies show that ideational robustness is important but also that its study must be combined with that of other policy mechanisms and processes in order to explain overall policy change patterns.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".