<b>Perspective</b>—The Interdependence of Private and Public Interests
Bibliographic record
Abstract
The predominant focus in research on organizations is on private or public institutions without consistent consideration of their interdependencies. The emphasis in scholarship on private or public interests has strengthened as disciplinary and professional knowledge has deepened: Management scholars, for example, tend to consider the corporation as the unit of analysis, whereas scholars of public policy often analyze governmental, multilateral, community, and nonprofit organizations. This article advocates a partial merging of these research agendas on the grounds that private and public interests cannot be fully understood if they are conceived independently. We review three major areas of activity today in which public and private interests interact in complex ways and maintain that current theories of organization science can be deployed to understand these interactions better. We also suggest that theories of public-private interaction require development and describe a concept called global sustainable value creation, which may be used to identify organizational and institutional configurations and strategies conducive to worldwide, intertemporal efficiency and value creation. We conclude that scholarship on organizations would advance if private-public interactions were evaluated by the criterion of global sustainable value creation, and we identify organizational research opportunities that jointly consider public and private interests.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".