Modelling capability-based risk allocation in PPPs using fuzzy integral approach
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
Appropriate risk allocation and sharing are significant critical success factors for public-private partnership projects, but evidence suggests that poor risk allocation practices prevail. This signifies the need to develop a robust model for assisting stakeholders in risk allocation decision-making. A non-additive fuzzy integral based multiple attribute risk allocation decision approach is proposed to effectively aggregate each stakeholder’s risk management capability assessment on accepted risk allocation principles that are derived from qualitative judgements and experience based knowledge of experts. Data collected from privately financed and developed power and transport infrastructure projects in Pakistan are used to demonstrate and validate the model for key risk factors that exhibit variable risk allocation preferences. Comparison of results with an additive aggregation approach confirms suitability of the adopted methodology as it performs better when modelling risk allocation preferences of experts due to its ability to handle interdependencies in the risk allocation criteria. Apparently, the allocation and sharing of key risks is significantly influenced by market, sector and project contexts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it