Risk Management in Project Networks: An Information Processing View
Bibliographic record
Abstract
Increasingly, projects are executed by networks of organizations. The networked form of organization has many important implications for project risk management. Information processing theories introduce mechanisms for processing information inside organizations as well as among organizations to reduce the uncertainty and equivocality inherently present in international projects. This study aims to examine the risk management practices involved at a project network level through an empirical analysis of one complex large project network executed in a challenging institutional environment. With regard to network level risk management, the paper identifies eight formal information processing mechanisms for implementing risk management: (1) established rules and criteria for the selection of subcontractors at a global level, (2) specification of responsibilities in the contract, (3) formal risk sheet, (4) progress follow-up tool, (5) database for project information, (6) customer reporting system, (7) updated project plan after the project is delayed, and (8) country study team. Personal relationships between parties, personal commitment, experienced individuals, and face-to-face meetings are identified as informal information processing mechanisms used as measures of project risk management to reduce equivocality. We also elaborate the fitness of the mechanisms used for the contextual situations of the project network settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".