Knowledge-based risk identification in infrastructure projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective risk management is a central function in the successful planning and execution of large infrastructure projects. This paper explores how current knowledge-based approaches for risk management can be improved upon so that they are more responsive to the attributes of a project and the needs of system users. A review of existing knowledge-based systems for risk management provides a backdrop for a discussion on desirable characteristics of such an approach. The proposed methodology adopts a model-based technique in that explicit abstractions of project components and processes, and the physical, regulatory, political, social, financial, economic, contractual, and organizational environments in which they are located, are created to assist in the reasoning about possible risks. This contrasts with several current systems that use only implicit representations. The reasoning schema and models of the physical project and environment that are used for the reasoning process are described in the paper.Key words: risk identification, project modeling, knowledge management, infrastructure projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it