Navigating the politics of evidence-informed policymaking: strategies of influential policy actors in Ontario
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
Abstract Policy studies suggest that evidence-informed policymaking (EIPM) requires framing and persuasion strategies, and an investment of time to form alliances and identify the most important venue. However, this advice is very broad and often too abstract. In-depth case studies help make this advice more concrete. To understand the engagement strategies of influential policy actors, this case study examines the Ontario Poverty Reduction Strategy, a large-scale provincial policy touted as “evidence-based.” The study is based on interviews with elite policy advisors ( n = 19) serving in different stages of the policymaking process. It shows that the elite advisors effectively used persuasion tactics, networking and longevity strategies to counteract a volatile political context and competing policy priorities. In light of the findings, this paper provides practical recommendations on how evidence producers can emulate such success in different contexts: understand formal and informal processes, master and exercise political acuity, and strategically establish networks with a diverse group of policy actors in order to effectively frame and communicate evidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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