A (meta)governance framework for multi-level governance of inter-organizational project networks
Bibliographic record
Abstract
Inter-organizational networks are frequently used to execute large and megaprojects. This study develops a theoretical framework for the governance of these networks. Twenty-eight case studies, each representing a network for a project, were assessed using 124 interviews in ten countries. A three-layer governance model is derived from the analysis. At the lowest layer (network governance) is the individual network of organizations collaborating in a project. This layer is explained through Multi-level Governance Theory. The intermediate layer (governance of networks) addresses the steering of the different networks these organizations are part of, such as for training, certification, safety etc.. At the top layer (metagovernance) are the ground-rules, set by governments or other investors to regulate how the two other layers are allowed to set up their governance. The study’s resulting theory combines three so far separate levels of governance into an overall understanding of large inter-organizational networks for projects. It provides parameters for practitioners to optimize their networks for better project results.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".