Designing for adaptation: Static and dynamic robustness in policy‐making
Bibliographic record
Abstract
Abstract Policy tools are chosen and deployed in the expectation that they will continue to work effectively over extended periods of time. This is a tall expectation to meet, given that the nature of policy problems and their contexts change constantly. To continue to operate effectively in the face of these changes and respond to policy feedback from policy actors and outputs, policy mixes must be robust. This robustness is of two types: static robustness in which policy means adapt while policy goals remain unchanged, and dynamic robustness in which both goals and tools change. The first equates robustness with resilience—that is, the ability to bounce back to a previous state and attain original goals in altered contexts caused by some change in internal or external conditions. The second, however, is more complex as it can involve changes in aspects of policy goals as well as means in order to allow policies to adapt more broadly by altering their form in response to changing circumstances. This second type of “dynamic robustness” focuses attention on the need for agility and upon the requisites for the creation of policy designs which allow for substantive changes in form as well as state. The article lays out these concepts and their interrelationships and the kinds of procedural and other tools involved in achieving either. It illustrates their features and differences using examples from different sectoral cases.
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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 | 0.039 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".