Twenty Years of Comparative Policy Analysis: A Survey of the Field and a Discussion of Topics and Methods
Bibliographic record
Abstract
Comparative policy analysis has emerged as a distinct field of study in the past two decades, however only one journal is explicitly devoted to its study: the Journal of Comparative Policy Analysis: Research and Practice (JCPA). This article performs a content analysis of abstracts in the field of comparative policy analysis to determine the contours of the field. First, the paper charts the development of comparative policy analysis and compares the trajectories of comparative politics, comparative public administration and comparative policy analysis. Second, the paper analyzes the results of the abstract coding to reveal the methods employed, countries studied, the number of countries studied and the countries of the authors’ institutions. The results from JCPA and other journals in the Elton B. Stephens Co. (EBSCO) Academic Complete database are compared to distinguish the dominant role that JCPA plays in the field. Finally, it discusses the major trends in the work published in comparative policy analysis, the limitations of the current publications and further areas for development. A clear finding is that the field of comparative policy analysis is on a sharp upward trajectory.
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
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 | 0.125 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.039 | 0.067 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".