The challenges of fractionalized property rights in public‐private hybrid organizations: The good, the bad, and the ugly
Bibliographic record
Abstract
Abstract Policy designers seeking to harness profit‐driven efficiency for public purposes are increasingly creating organizations with fractionalized property rights that distribute “ownership” among public and private actors. The resulting hybrids are quite diverse, including mixed enterprises, public‐private partnerships, social entrepreneurship organizations, government‐sponsored enterprises, and various other hybrid forms. Marrying public purposes to private sector efficiency and strategic flexibility provides a tempting rationale for mixing public and private owners in hybrid organizations. Because public‐private hybrids involve fractionalized property rights, however, they exhibit tension among owners over both strategy and, more importantly, goals. To understand public‐private hybrids, we assess them in terms of six dimensions of property rights: fragmentation of ownership, clarity of allocation, cost of alienation, security from trespass, credibility of persistence, and autonomy (of both owners and managers). The unclear allocation of fractionalized ownership rights facilitates the appropriation of financial residuals and asset ownership opportunistically. Other weaknesses in the property rights configurations of public‐private hybrids create managerial dissonance or opportunistic behavior that typically leads to a narrowing of goals, but sometimes also to organizational failure.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".