Evidence-informed policymaking and policy innovation in a low-income country: does policy network structure matter?
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.
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 | 0.032 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".