Editorial introduction: Advancing policy frontiers—Governance, learning, and innovation in policy studies
Bibliographic record
Abstract
Welcome to the first issue of Policy Studies Journal (PSJ) for 2025!This issue features 11 exceptional contributions-seven regular articles and four research notes, showcasing a range of topics that enhance our understanding of policy processes and outcomes.Each article reflects the journal's commitment to fostering rigorous research and innovative analysis in policy studies.As we begin this new volume, we are pleased to announce some changes within our editorial team.Dr. Heasun Choi from the University of Arkansas and Dr. Davor Mondom from Syracuse University have stepped down from their roles as Managing Editors.Their dedication and leadership have been instrumental in shaping the journal's direction, and we extend our sincere gratitude for their contributions.To support the journal's continued growth, we are excited to welcome Ms. Erica Ivins from Syracuse University as our new Managing Editor.We also welcome three new Editorial Assistants from the University of Arkansas-Ms.Rinjisha Roy, Ms. Annette Nyoni, and Mr. Travis Wagher.Their skills and enthusiasm will undoubtedly strengthen the editorial team and help us maintain the high standards our contributors and readers have come to expect.The year 2024 was a milestone year for PSJ, marked by several remarkable achievements that underscore the journal's extended influence and the vibrancy of the policy community.The number of submissions reached an unprecedented high, with 615 new manuscripts submitted, compared to 498 in 2023 and an average of 350 annually in prior years.This surge is a testament to the journal's reputation as a leading outlet for high-quality policy scholarship.We are deeply grateful to all the authors who chose to submit their work to PSJ.This increase in submissions was accompanied by a significant expansion in our peer review process.In 2024, we sent out over 2290 invitations to referees, representing a 122% increase from the previous year.Approximately 38% of invited reviewers submitted thoughtful, constructive, and detailed feedback, which authors have frequently acknowledged as invaluable, regardless of the outcome of their submissions.The contributions of these referees are essential to maintaining the integrity and quality of PSJ publications.Notably, the USA, Germany, China, the UK, Switzerland, Canada, Sweden, Italy, the Netherlands, and Denmark were the top contributing countries to our review process.We extend our deep gratitude to all reviewers for their expertise and dedication to advancing the field.PSJ also made significant strides in publication output in 2024, with a total of 55 articles published-a 67% increase compared to the previous year.Nearly 45% of these articles were made openly accessible, reflecting our commitment to broadening the reach and impact of policy research.In addition, PSJ achieved a new milestone in readership.Articles from the journal were accessed over 314,060 times in 2024, a significant increase from 208,300 full-text views in 2019.This growing engagement highlights the global reach and impact of the journal.The top countries for readership included the USA, UK, China, Australia, Germany, the Netherlands, Canada, Sweden, South Korea, and Hong Kong.We are thrilled to see PSJ articles resonating with readers worldwide and contributing to critical policy discussions.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".