Challenges in applying design thinking to public policy: dealing with the varieties of policy formulation and their vicissitudes
Bibliographic record
Abstract
Policy design is a type of policy formulation activity centred on knowledge application in the creation of policy alternatives. Expected to attain public sector goals and government ambitions in an effective fashion, it can be undertaken many different ways. The current literature on policy design features an ongoing debate between adherents of traditional approaches to the subject in the policy sciences and those importing into policymaking the insights of design practices in other fields such as industrial engineering and product development: ‘design-thinking’. Issues examined in more traditional approaches to policy design are very wide-ranging and address a wide variety of formulation modalities and their strengths and weaknesses. Efforts to promote ‘design-thinking’ in the public policy realm, on the other hand, focus on policy innovation and rarely deal with issues such as the barriers to implementation, political feasibility or the constraints under which decision-making takes place. This article discusses these differences and argues adherents of design-thinking need to expand their reach and consider not only the circumstances facilitating the generation of novel ideas but also the lessons of more traditional approaches concerning the political and other challenges faced in policy formulation and implementation.
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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 | 0.147 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.167 |
| Scholarly communication | 0.043 | 0.043 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".