Do Networks Matter? Linking Policy Network Structure to Policy Outcomes: Evidence from Four Canadian Policy Sectors 1990-2000
Why this work is in the frame
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Bibliographic record
Abstract
Relatively recent contributions to the policy literature have called into question the utility of the "network" approach to the study of public policy making, including a challenge to long-held views concerning the impact of the structure of policy subsystems on policy change. This article uses empirical evidence accumulated from case studies of four prominent Canadian federal policy sectors over the period 1990-2000 to address this issue. It sets out a model that explains policy change as dependent upon the effects of the articulation of ideas and interests in public policy processes, and generates several hypotheses relating different subsystem configurations to propensities for paradigmatic and intra-paradigmatic policy dynamics. It suggests that the identification of the nature of the policy subsystem in a given policy sector reveals a great deal about its propensity to respond to changes in ideas and interests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it