Varieties of collaboration in public service delivery
Bibliographic record
Abstract
Collaboration – and its cognates consultative in-house service delivery, contracting out, commissioning, co-management, co-production, and third party certification – have in recent years been at the center of efforts to reform the public sector and devolve its capacity for policy implementation and service delivery. While the arguments in support of the use of different types of collaborative service delivery are plausible and the intentions motivating them laudable, the crucial questions to ask are: what kind of service delivery arrangement is “collaborative?” And, when could such an arrangement be used? Seeking answers to posed questions this article, and articles in the special issue it introduces, conceptualize and explore alternative arrangements in public service delivery by investigating them though governance lenses. After addressing the nature and collaborative potential for each type of service delivery, the article situates them in the model of capacity combining analytical, managerial, and political competences over three levels of governance activities. It shows that while the success of all collaborative arrangements for public service delivery is linked to political capacities, each arrangement involves a critical type of managerial or analytical capacity which serves as its principle vulnerability. The extent to which various collaborative arrangements can address these vulnerabilities is assessed along with their design requisites and potential utility.
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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".