Bibliographic record
Abstract
Policy design: From tools to patchesPolicy design involves the purposive attempt by governments to link policy instruments or tools to the goals they would like to realize.The study of policy design focuses on these tools, their advantages and disadvantages and better understanding the processes around their selection and deployment in order to improve policy-making efforts and outcomes.The roadmap for the development of this approach to the policy sciences stretches from early works in public policy studies around the identification of policy tools and the classification of instrument types in the 1960s and early 1970s (Design 1.0), to present-day studies that strive to effectively formulate effective and context-appropriate policy alternatives given the specific historical legacies and political realities in which policy selection and implementation takes place (Design 2.0).Canadians have been leaders in both eras, with many well-known works on policy tools as well as more recent works on policy design written by Canadian authors.This contribution sets out five key sets of articles in each era in this field, featuring a major work in the discipline and a matching article from Canada in each time period examined.We have chosen to organize the discussion below chronologically featuring the two major policy design "eras" and the major theoretical developments that have defined them.Design 1.0: the identification of policy tools
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 | 0.067 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.011 | 0.075 |
| Scholarly communication | 0.034 | 0.027 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 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".