The Science–Policy Relationship Hierarchy (SPRHi) model of co-production: how climate science organizations have influenced the policy process in Canadian case studies
Bibliographic record
Abstract
Can better-functioning science–policy relationships (SPRs) address the seeming discrepancy between the scientific consensus on climate change and the insufficient ensuing policy outcomes? Certain scholarly works on science–policy interfaces and evidence-based policy are optimistic, while the literature on research utilization is pessimistic. The field of science, technology, and society and the concept of co-production advance a broader view, suggesting that more holistic (i.e., institutional or systemic) changes may offer a way forward. This article synthesizes causal claims from such literatures into an analytical framework of potential pathways from co-productive SPR characteristics to policy action. It then investigates, through expert interviews, three climate SPRs in Canada: a municipal-level case between the Pacific Climate Impacts Consortium and local communities, a provincial-level case between the Pacific Institute for Climate Solutions and the Climate Action Secretariat, and a national-level case between the Canadian Foundation for Climate and Atmospheric Sciences and the federal government. In light of the analytical framework, the cases suggest a theoretical hierarchy of function for SPRs: incidental interaction (at the bottom), basic partnership, interactive dialogue, and true co-production (at the top), each of which can be coupled with a supplementary network (to the side). This template is presented as the Science–Policy Relationship Hierarchy model. Collectively, the cases and the model reveal causal pathways that may explain why any given SPR ends up functioning the way it does (e.g., external political conditions are important), implying prescriptions for improvement. Besides the analytical framework and model, the main contribution is the finding that co-productive strategies are unlikely to lead to concrete policy changes on their own, but are crucial for cultivating soft policy influences and side benefits.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.036 | 0.032 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".