Designing for robustness: surprise, agility and improvisation in policy design
Bibliographic record
Abstract
ABSTRACT How best to deal with uncertainty and surprise in policy-making is an issue which has troubled policy studies for some time. Studies of policy uncertainty and policy failure have emphasized the need to create policies able to be improvised upon in the face of an uncertain future, meaning there is a need to design and adopt policies featuring agility, and flexibility in their components and processes. Such policies require redundant resources and capabilities and this need is in strong opposition to ideas about design which equate better designs with efficiency, implying the allocation of only the minimum amount of resources possible, and which also often emphasize routinization and the replication of standard operating procedures and programme elements in order to ensure consistency in programme delivery. While these latter designs may be appropriate in stable circumstances or where competition can provide a degree of system-level resilience, this is not true for many public sector activities where government is the sole provider of particular goods and where services and future scenarios are unknown, contested or unpredictable. As studies of crisis management and other similar situations have emphasized, in these instances robustness is needed and can be planned for. This article examines the concepts of surprise, agility and improvisation and their linkages to robustness in order to both clarify terminology and outline the organizational and managerial features of policies and policy-making which prevent, and facilitate, flexible adaptation in both policy content and processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".