The distribution of analytical techniques in policy advisory systems: Policy formulation and the tools of policy appraisal
Bibliographic record
Abstract
The literature on policy analysis and policy advice has not generally explored differences in the analytical tasks and techniques practiced within government or between government-based and non-government-based analysts. A more complete picture of the roles played by policy analysts in policy appraisal is needed if the nature of contemporary policy work and formulation activities is to be better understood. This article addresses both these gaps in the literature. Using data from a set of original surveys conducted in 2006–2013 into the provision of policy advice and policy work at the national and sub-national levels in Canada, it explores the use of analytical techniques across departments and functional units of government and compares and assesses these uses with the techniques practiced by analysts in the private sector as well as among professional policy analysts located in non-governmental organizations. The data show that the nature and frequency of use of the analytical techniques used in policy formulation differs between these different sets of actors and also varies within venues of government by department and agency type. Nevertheless, some general patterns in the use of policy appraisal tools can be discerned, with all groups employing process-related tools more frequently than “substantive” content-related technical tools, reinforcing the procedural orientation of much contemporary policy work identified in earlier studies.
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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 | 0.310 | 0.501 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.039 | 0.039 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.036 | 0.028 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".