From Niches to the Mainstream: A Computational Review and Research Agenda for Policy Innovation
Bibliographic record
Abstract
Innovation in public policy is a rapidly expanding field, highlighting its critical role in addressing complex societal challenges. This bibliometric review (n = 17,264) synthesizes over 50 years of scholarship, systematically identifying key gaps, including conceptual fragmentation, limited theoretical engagement, methodological conservatism, and geographic and sectoral imbalance, and underutilization of the comparative perspective. These challenges constrain the field’s ability to address pressing global issues such as climate change adaptation, digital transformation, economic inequality, and the energy transition. This study proposes a comprehensive research agenda to bridge these gaps and move the field from niches toward more inclusive, integrative, and rigorous mainstream scholarship.
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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 | 0.033 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.027 | 0.044 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".