Bibliographic record
Abstract
The article is aimed at increasing the probability of successful IT project completion by identifying the sources of 105 universal risks as well as establishing cause-and-effect relationships between these risks. The article presents the results of an analysis of 105 risks relevant to IT projects; five of them are commercial risks, 45 are compliance risks and 55 are project risks. Risk analysis was carried out using the 5Why, SWIFT and Harrington coefficients. Based on the results of the analysis, the root causes initiating the onset of risks were identified, such as the user, customer, project manager, project team, subcontractor and competitor. Moreover, it was found that the share of the users in the total number of risk sources is 2%, 15% for the customer, 43% for the project manager, 36% for the project team, 2% for the subcontractor and 2% for the competitor. The article also shows models of cause-and-effect relationships of compliance and project risks, presents the results of assessing the risks occurrence probability and their possible impact in cases of materialization, and establishes the most likely and dangerous scenarios in IT projects. The results obtained allowed the development of a criterion to assess the management maturity of a contractor (executor, supplier) planning to develop an computer program as part of an IT project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".