Risk Factors in IT Public–Private Partnership 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
Project managers from both the public and private sectors have always known that better project management is often synonymous with better risk management. As such, all projects are subject to risk factors that make their management more or less complicated. In this article, we contend that successful project managers need tools to better identify and assess project risks and we try to provide such a tool in the form of grid of specific risk factors. This article analyzes risk factors in IT projects conducted using public–private partnership (PPP) procurement from the public partner’s perspective. Our research uses, as empirical case studies, three projects undertaken by the Tunisian government in partnership with IT and engineering companies. The results reveal 13 specific risk factors, which are classified into three generic risk factor categories: strategic, operational, and key resources. The adverse effects of risks materializing are also identified and analyzed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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