Assessing Uncertainty and Risk in Public Sector Investment 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
The feasibility and profitability of large investment projects are frequently subject to a partially or even fully undeterminable future, encompassing uncertainty and various types of risk. We investigate significant issues in the field of project appraisal techniques, including risks and uncertainties, appropriate risk analysis, project duration as well as the dependencies between (sub-) projects. The most common project appraisal techniques are examined addressing benefits and weaknesses of each technique. Furthermore, the practical use of the different techniques for the public sector is examined, exemplifying this with a small-scale analysis of the risk analysis procedures of the World Bank. Our finding suggest that in particular for the public sector, practical implementation of quantitative techniques like Monte Carlo simulation in the appraisal procedure of investment projects has not fully occurred to date. We strongly recommend further application of these approaches to the evaluation of processes and financial or economic risk factors in project appraisal of public sector institutions.
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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 | 0.001 |
| 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