A theory of policy advisory system quality: Hirschman 2.0 or what makes for good policy advice?
Bibliographic record
Abstract
Not everyone’s ideas count equally in terms of influencing and informing policy design and instrument choices. As the literature on policy advice has shown, such advice arises from many different actors interacting with each other often over relatively long timeframes. Actors within these ‘policy advisory systems’ operate within the confines of an existing set of political and economic institutions and governing norms, and each actor brings with them different interests, ideas and resources. Studying who these actors are, how they act and how their actions affect the overall nature of the advice system and its contents are critical aspects of current public policy research. But not all these elements have been equally well conceptualised or studied, especially those concerning their impact on the quality of policy advice emerging from a system. In this article, the general nature of policy advisory systems is set out, their major components described and a model of individual and organisational behaviour within them outlined inspired by a modification of the ‘exit, voice, loyalty’ rubric of Albert Hirschman. Our findings show how aggregated individual organisational behaviour along the lines suggested by Hirschman can over time result in very different kinds of advice being provided by an advisory system, with predictable consequences for its nature and quality.
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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 | 0.028 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".