A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations
Bibliographic record
Abstract
Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants’ responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".