Appointed political staffs and the diversification of policy advisory sources: Theory and evidence from Canada
Bibliographic record
Abstract
Abstract Appointed political staffs were featured in the initial elaboration of the ‘policy advisory systems’ (PAS) model yet have received considerably less attention than other components. This article revisits the PAS model and argues that political staffs engage in important procedural advisory activities masked by the PAS focus on location and control. The principle contention being that political staffs influence within advisory systems may also be a product of their procedural brokerage of other sources of policy advice. The article advances a conceptual framework to understand political staffs brokerage as ‘bridging’. Setting out ‘positive’ and ‘negative’ forms that can be arrayed along ‘administrative-technical’ and ‘partisan-political’ types. A Canadian sub-national case study is examined using the framework revealing variance in the type and nature of bridging based on institutional location of political staffs and the specific brokerage tasks they undertake. First minister's office bridging is found to be considerably more limited than that undertaken by minister's office political staffs, particularly in relation to the bridging of exogenous sources of policy advice. The framework and empirical findings enrich the policy advisory systems literature by demonstrating the importance of coupling spatial considerations with attention to the actual tasks of advisory system members. Additionally, highlighting the importance of procedural policy advisory brokerage as a source of influence within advisory systems.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".