Teaching Policy Design: Themes, Topics & Techniques
Bibliographic record
Abstract
After briefly discussing the origins of the policy design field, this paper examines aspects of design paedagogy in policy schools and programmes.It sets out a series of topics which University level courses typically cover, explains their importance to the field, and what is typically addressed in coursework.These include subjects such as: what is policy design and how it has evolved; introducing policy tools and portfolios; issues around persuasive design, targeting and compliance; who are the policy designers and how do they think and operate; what is meant by policy effectiveness; what are design best practices; how designs and designers deal with uncertainty, conflict and controversy; and what are the future directions in which the field is heading, why and what this means for both design paedagogy and practice.The chapter then turns to paedagogical techniques deployed in these courses across four continents, dealing with differences between undergraduate and graduate level instruction, case-based instruction and on-line and distance learning, as well as efforts to integrate co-design and innovative pedagogies including new methods and techniques such as Big Data methodologies and policy labs and experiments.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".