Risk management applied to projects, programs, and portfolios
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a review of recent risk management literature applied to projects, programs and project portfolios performed inside an organization with the aim of finding areas of opportunity to continue research and the development of current guides and methodologies. Design/methodology/approach The paper uses a review of recent literature published by international organizations and journals specializing in the field of project, programs, and portfolios. Findings The review shows that project risk management is a well developed domain in comparison to the program risk management and portfolio risk management fields, for which specifically written methodologies are difficult to find. The review also demonstrates the need to include better tools to perform a continuous control and monitoring process. Integrating a vulnerability approach is also necessary in order to consider the project, program or portfolio characteristics which mediate between consequences and the exposure to hazards and opportunities. Research limitations/implications The review does not consider white papers or popular media. Originality/value The limitations found in current risk management methodologies show the challenges researchers must undertake to continue improving this domain for projects performed inside an organization. The paper exhibits the areas of opportunity where methodologies and guides can be further improved to evolve towards better management structures.
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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".