Policy formulation, governance shifts and policy influence: location and content in policy advisory systems
Bibliographic record
Abstract
Abstract Most studies of policy formulation focus on the nature and kind of advice provided to decision-makers and think of this as originating from a system of interacting elements: a “policy advisory system”. Policy influence in such models has historically been viewed as based on considerations of the proximate location of policy advisors vis à vis the government, linked to related factors such as the extent to which governments are able to control sources of advice. While not explicitly stated, this approach typically presents the content of policy advice as either partisan “political” or administratively “technical” in nature. This article assesses the merits of these locational models against evidence of shifts in governance arrangements that have blurred both the inside vs outside and technical vs political dimensions of policy formulation environments. It argues that the growing plurality of advisory sources and the polycentrism associated with these governance shifts challenge the utility of both the implied content and locational dimensions of traditional models of policy advice systems. A revised approach is advanced that sees influence more as a product of content than location. The article concludes by raising several hypotheses for future research linking advisory system behaviour to governance arrangements.
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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 | 0.018 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".