Evidence-informed policymaking and policy innovation in a low-income country: does policy network structure matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The application of social network analysis to policy networks continues to grow, including the application of social network analysis tools and concepts in order to explain policy outcomes. Gaps in this field of study persist in terms of both policy issues studied, as well as types of polities or networks analysed. This study extends previous research on the role of network structure in shaping policy outcomes by analysing network structure’s effect on the use of research evidence by three health policy networks in Burkina Faso, a low-income West African country, and the resulting innovativeness of the policies made. This comparative case study confirms certain hypotheses related to the effect of network closure and heterogeneity on evidence use and innovation; namely, that heterogeneous networks are more likely to be exposed to new ideas, and thus to use research evidence and adopt innovative policies. High levels of centralised control and power may support innovation when the new ideas are consistent with the dominant network paradigms; otherwise, new ideas may receive less traction. These findings confirm previous research and point to opportunities to shape networks to achieve innovation and policy change based on the best evidence.
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.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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