Use of research evidence in legislatures: a systematic review
Bibliographic record
Abstract
Background: Although lawmakers play an essential role in policymaking, there is no systematic review on the use of research evidence in legislatures. Aims and objectives: To examine types of research use and factors facilitating and hindering use in legislatures. Methods: We conducted a systematic review of studies in legislatures, regardless of geographical region or year of publication. We included empirical studies irrespective of the methodology employed. Thematic synthesis was used to synthesise the type of use and the facilitating and hindering factors to using research evidence in parliaments. We included 21 studies. Findings: The most frequently observed type of utilisation was the use for symbolic or tactical purposes. Forms of use specific to legislatures were also identified, such as to prepare questions and debates and to help build consensus. Four categories of factors seen as facilitators or barriers were found: institution and organisation, research characteristics, policy and political context, and individual characteristics. Some factors had already been identified in previous reviews, while others seem to apply exclusively to legislatures. Discussion and conclusions: The review identified types of use of research evidence observed in legislatures and developed a new categorisation of factors that may promote or hinder evidence use in this institutional setting. It highlighted a need for more research beyond the US, in unicameral legislatures and in countries with a parliamentary form of government. Content analysis of parliamentary debates in legislative assembly or committee to examine the use of research evidence seems to be underused.
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
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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Scholarly communication Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.106 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.035 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".