Public Capacity, Plural Forms of Collaboration, and the Performance of Public Initiatives: A Configurational Approach
Bibliographic record
Abstract
Abstract We assess conditions that explain plural forms of public and private action using a comparative study of 24 public initiatives in Brazil, India, and South Africa. Measuring performance as evidence of positive outcomes to their target populations, we compare cases of high and low performance. Our configurational approach examines combinations of conditions leading to positive outcomes: public operational capacity, diverse collaborations nurtured by public units (with for-profit firms, with nonprofit organizations, and with other units in the public bureaucracy), and stakeholder orientation (permeability to multiple sources of input to design and adjust the project). We apply fuzzy set qualitative comparative analysis to unveil configurations consistent with high performance. Our configurational analysis reveals two distinct paths to high performance. A path with higher private engagement involves concurrent collaborations with for-profit and nonprofit actors, whereas an alternative path with higher internal (public) engagement relies on collaborations within the public bureaucracy complemented by high permeability to inputs from multiple stakeholders. Our results also confirm that strong public capacity is necessary in all high-performance configurations. An important implication is that externalization and multiple forms of collaboration are not substitutes for weak governments. Furthermore, our configurational perspective contributes to the literature by operationalizing a multiple-actor, multiple-logic perspective describing alternative paths to high performance.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".