Trajectories of Value Generation and Capturing by Public–Private Hybrids: Mechanisms of multi-level governance in healthcare
Bibliographic record
Abstract
Our paper unpacks the multi-level process through which hybrids generate value in the long term. By combining interviews, archival and survey data, we examine the longitudinal trajectories of value generation of public–private hybrids (PPHs) established in the regionalized Italian healthcare system (1992–2018). We identify three ideal-type trajectories: (a) long-term value generation by stable PPHs; (b) long-term value generation by transient PPHs; and (c) interrupted value generation by terminated PPHs. We uncover how these trajectories are shaped by the interplay between regional governance arrangements – i.e. institutionally embedded norms regarding who is entitled to generate value for the field, the scope for organizational value capturing and the institutional monitoring system – and organizational governance mechanisms – i.e. the strategic orientation of individual PPHs and their internal monitoring functions. Our paper contributes to theory by conceptualizing the mechanism of ‘double filter’, which we define as the set of field- and organizational-level governance compensatory mechanisms that, together, allow long-term value generation by hybrids. We also problematize the relationship between hybrids’ value-generation capacities and the persistence of hybrid organizational forms, and ultimately trace it to different field governance arrangements. In so doing, we conceptualize ‘transient hybrids’ as a distinctive organizational form for long-term value generation.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".