The Application of Project Management Standards and Success Factors to the Development of a Project Management Assessment Tool
Bibliographic record
Abstract
In spite of all that is known about project management best practices, they are often absent from typical construction projects. This has motivated our interest in developing a tool to assess construction project management practices, focusing on the assessment of individual project practices. We will also explore project outcomes and their correlation with project management practices-potentially identifying project management value. Previous efforts have addressed project management assessment. The paper describes examples that assess an individual's project management skills and approaches that examine the project management competencies of organizations. In contrast to these, our focus is on assessing the project management practices that have been implemented for specific construction projects. A central component of any assessment scheme is the identification of specific elements to be assessed (the assessment “targets”). We intend to draw heavily upon established project management standards and project success factors from previous research to provide the specific targets and benchmarks to be assessed. These include the Project Management Body of Knowledge (PMBOK) by the PM Institute, the IPMA Competence Baseline (ICB) by the International PM Association, ISO 9000, and Prince2 by The Office of Government Commerce UK. This paper describes how these standards are integrated into the project management assessment tool. It discusses the theoretical foundations for the project management assessment tool and the methodologies used for developing the tool and for applying the tool to specific project situations.
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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 | 0.047 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".