Collaborative innovation in the public sector and the policy design dilemma: a promising analytical perspective
Bibliographic record
Abstract
Collaborative Public Sector Innovation (CPSI) is emerging as a new concept aimed at transforming public sector policymaking in a post-New Public Management (NPM) world. It harnesses the potential of emerging governance networks to foster innovation and generate public value. Despite a growing interest among public policy and public administration scholars, CPSI remains an underexplored framework for public sector innovation, particularly regarding its relevance to policy design. This article evaluates this emerging theoretical framework and offers recommendations to advance the analysis of CPSI as a novel approach to public sector reforms. Drawing on the findings from the articles comprising this special collection, it also highlights the theoretical, analytical, and empirical challenges that lie ahead for the consolidation of CPSI, encouraging debate among both academics and policy practitioners. The discussion, structured around a set of identified challenges for the development of CPSI, reflects not only on the concept itself and the theories that support this emerging post-NPM approach to public administration reforms, but also on its relationships with policy design and policy implementation. Additionally, the article examines the added value that specifically designed arrangements should bring to public sector innovation outputs and outcomes.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.079 |
| Scholarly communication | 0.033 | 0.028 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".