Policy advisory systems and public policy making: Bibliometric analysis, knowledge mapping, operationalization, and future research agenda
Bibliographic record
Abstract
Abstract This study offers a comprehensive overview of the literature on policy advisory systems (PAS), motivated by its significance, growing interest in the field among policy scholars, and the substantial body of recent research published recently. The study conducted a quantitative bibliometric analysis of 62 articles on PAS published between 1993 and 2022, using data extracted from the Web of Science and Scopus databases. The analysis included performance analysis, collaboration analysis, and science mapping (co‐citation and co‐word analysis). The dataset comprises articles published in 29 different journals, with 17.2% of these journals publishing three or more articles on PAS. Of the 80 authors in the dataset, 78.8% had only one publication. Over the past 5 years, there has been a 69% increase in the production of research on PAS, with the majority of output coming from Canada, Australia, Germany, and the Netherlands. The intellectual structure of the field was examined through co‐citation analysis, revealing two main clusters named dynamics of PAS and Policy Advice & Policy Analysts' Role. The former focuses on the variations and dynamics of PAS, while the latter is more concerned with the nature of policy advice and the role of policy analysts. The co‐word analysis identified the most relevant issues linked with PAS, including policy advice, externalization, politicization, and policy capacity. The study has also provided operationalization of the concept with the support of empirical evidence and categorized it into internal and external PAS. Externalization is the dominant trend in Anglophone countries, while public service in Europe and Canada has been the focus of internal PAS research. The study identifies gaps in the literature and calls for further research on the application of PAS in the Global South, the demand for policy advice, and comparative analysis of PAS across different contexts and dimensions.
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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 | 0.052 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.080 | 0.195 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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, 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".