The dual dynamics of policy advisory systems: The impact of externalization and politicization on policy advice
Bibliographic record
Abstract
Abstract The concept of “policy advisory systems” was introduced by Halligan in 1995 as a way to characterize and analyze the multiple sources of policy advice utilized by governments in policy-making processes. The concept has proved useful and has influenced thinking about both the nature of policy work in different advisory venues, as well as how these systems work and change over time. This article sets out existing models of policy advisory systems based on Halligan's original thinking on the subject which emphasize the significance of location or proximity to authoritative decision-makers as a key facet of advisory system influence. It assesses how advisory systems have changed as a result of the dual effects of the increased use of external consultants and others sources of advice — ‘externalization’ — and the increased use of partisan-political advice inside government itself — ‘politicization’. It is argued that these twin dynamics have blurred traditionally sharp distinctions between both the content of inside and outside sources of advice and between the technical and political dimensions of policy formulation, ultimately affecting where influence in advisory systems lies.
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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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".