Challenges in applying design thinking to public policy: dealing with the varieties of policy formulation and their vicissitudes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it