A collaboratively derived international research agenda on legislative science advice
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 The quantity and complexity of scientific and technological information provided to policymakers have been on the rise for decades. Yet little is known about how to provide science advice to legislatures, even though scientific information is widely acknowledged as valuable for decision-making in many policy domains. We asked academics, science advisers, and policymakers from both developed and developing nations to identify, review and refine, and then rank the most pressing research questions on legislative science advice (LSA). Experts generally agree that the state of evidence is poor, especially regarding developing and lower-middle income countries. Many fundamental questions about science advice processes remain unanswered and are of great interest: whether legislative use of scientific evidence improves the implementation and outcome of social programs and policies; under what conditions legislators and staff seek out scientific information or use what is presented to them; and how different communication channels affect informational trust and use. Environment and health are the highest priority policy domains for the field. The context-specific nature of many of the submitted questions—whether to policy issues, institutions, or locations—suggests one of the significant challenges is aggregating generalizable evidence on LSA practices. Understanding these research needs represents a first step in advancing a global agenda for LSA research.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| 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 | 0.001 |
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 it