Networks and evidence-based advocacy: influencing a policy subsystem
Bibliographic record
Abstract
Background: Timely access to relevant and trustworthy research findings is an important facilitator of research use. But the relational aspects of evidence generation, mobilisation and use have been insufficiently explored. Aims and objectives: Our aim is to describe the strategic communicative and relational work of two intermediary organisations playing thought leadership roles within a large, heterogeneous and loosely configured network comprised of individuals and organisations from the following sectors: academia, frontline service delivery, philanthropic funding, advocacy organisations and government. Methods: The data for this project were generated as part of a study of the ways social science research influences policy, practice and systems-change processes. Proceeding from the standpoints of people who generate and/or engage with research in an effort to address homelessness in Canada, this article focuses on the intersections of research, strategic communication and policy making. Findings: Our findings suggest that strategic communication and knowledge exchange play integral roles in efforts to create evidence-based policy change. These communicative activities take the form of public-facing political and/or media engagement strategies, traditional knowledge mobilisation activities and continuous informal and timely exchanges of information between trusted allies. Discussion and conclusions: Our study reveals the importance of a heterogeneous network structure, with formal and informal alliances between individuals and organisations, as well as key intermediary organisations through which knowledge can be strategically mobilised within the network to serve policy change aims. Furthermore, our study suggests that interest in evidence-led governance is shifting the boundaries between research, advocacy and government action.
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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 | 0.087 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.058 |
| Scholarly communication | 0.031 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".