Kicked cans and poison pills: third generation policy advisory system studies and the management of quality political advice
Bibliographic record
Abstract
Abstract The decision-making process in government is influenced by a complex system of policy advice, which affects both the kinds of advice solicited and how it is received. Previous first and second generation studies of these policy advisory systems (PAS) focused primarily on identifying the structures and dynamics of advisory relationships in different contexts and jurisdictions. However, managing these systems to inform policy (in)action is now a major area of interest. This “3rd generation” of inquiry looks at system quality and how to measure and manage it. This includes how to manage political risk and inform strategies to address political concerns, such as deferring responsibility to future governments (“kicking the can down the road”) or leaving them unfunded mandates (“poison pills”). Third generation studies of policy advice systems need to consider how advice on such strategies is created and transmitted and not focus exclusively upon advice on management issues concerned with program efficacy and efficiency. That is, a high-quality PAS must manage both technical and political risks and deal with calculations of political blame and credit advice just as they have looked at the costs and benefits of alternate policy arrangements in the past.
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 | 0.057 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".