Bibliographic record
Abstract
The growing intensity and complexity of public service has spurred policy reform efforts across the globe, many featuring attempts to promote more collaborative government. Collaboration in Public Service Delivery sheds light on these efforts, analysing and reconceptualising the major types of collaboration in public service delivery through a governance lens. \n \nFeaturing careful analysis with a global scope, this book unpacks the concept of collaborative service delivery and its practice, drawing from the fields of public policy, public administration, and management. Chapters by leading authors in these areas address service delivery arrangements including co-production, co-management, consultations, contracting-out, commissioning and certification. With a keen focus on conditions that are critical for the success of such collaborative arrangements, as well as their different pathways and pitfalls, the authors suggest ways to improve the analytical, managerial and political capacities needed for successful collaboration in public service delivery. \n \nThis timely and comprehensive book is useful for students at all levels interested in public policy, governance, administration and management, as well as researchers investigating the governance of collaborative service delivery. Policymakers and practitioners working to re-evaluate and improve public service provision, especially, will also benefit from its insightful discussions of the conditions and mechanisms under which collaborative arrangements operate and fail or succeed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".