Public Managers in the Policy Process: More Evidence on the Missing Variable?
Bibliographic record
Abstract
Questions have been posed about the lack of knowledge of the role public managers play in the policy process. In this study, following on the suggestions of Hicklin and Godwin and Meier in this journal, we identify different dimensions of the analyst–manager divide among professional policy workers. Using the results of several recent large‐N surveys of Canadian federal, provincial, and territorial policy workers, we explore the roles each group plays in the policy analytical process and the variations in their behavior in terms of duties and tasks, attitudes, and interrelationships. We also examine these to see the impact of federalism on professional policy practices. The study uncovers three groups of policy workers and policy managers—coordinator‐planners, research‐analysts, and director‐managers. Differences between groups of policy workers are found for their policy‐related work and their perceptions of tools of policy effectiveness, and differences between levels of government are identified for issues of time demands and coordination and tools of policy effectiveness. The implications of these findings for the study of public managers in the policy process are considered in conclusion.
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 | 0.082 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".