Fuzzy Preference Relations Consensus Approach to Reduce Conflicts on Shared Responsibilities in the Owner Managing Contractor Delivery System
Bibliographic record
Abstract
This paper proposes a fuzzy preference relations consensus (FPRC) approach that helps owners and contractors reach consensus on their responsibilities and reduce conflicts in shared tasks. A fuzzy similarity consensus (FSC) model was developed to aggregate experts’ opinions on roles and responsibilities in the owner managing contractor (OMC) project delivery system. The FSC model categorized 324 generic OMC tasks into three responsibility task lists: owner, contractor, and shared. In a consensus-reaching process, the FPRC approach is applied to shared tasks, where expert opinions on responsibility conflict are expressed, to achieve an aggregated responsibility decision for each task. Experts compare the three responsibility alternatives in pairs by using linguistic preferences, defined on a fuzzy preference scale, to select a preferred responsibility alternative for each of the conflicting tasks. A computed linguistic consensus degree guides the experts on their level of consensus in every round of the process. The quality of experts is defined with a fuzzy expert system–determined importance weight factor for each expert. The FPRC approach is relevant to the construction industry, as it incorporates consistency in decision making by allowing experts to measure and reach an adequate level of consensus linguistically when deciding on responsibilities. The proposed approach provides a method of reducing conflicts in the assignment of task responsibility between the owner and its contractors as early as the project initiation phase; thus, the project teams can concentrate on the work to be done rather than deal with responsibility conflicts during project execution.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".