Bibliographic record
Abstract
Recently, ten knowledge areas (KAs) of project management have been published by the PMBOK® Guide.They comprise specific skills and experiences to ensure accomplishing project goals, and include management of: integration, scope, cost, time, quality, communications, procurement, risk, human resources and stakeholders.This research paper focuses on the ten required KAs for a project manager or a project to be successful.It aims at applying the Softcomputing modeling techniques to describe the relations between the 47 processes and the KAs.Such a model will enable users to predict the overall competencies of the project management.Thus, it provides an assessment tool to envisage, visualize and indicate the overall performance and competency of a project.The proposed Softcomputing Knowledge Areas Model (SKAM) is a two-stage model.The first stage involves ten models.Each model describes relations between a specific KA and its related processes.The outputs of these ten models will feed into the second stage that will represent the relationship between all the ten KAs and the overall predicted competencies of a project.A combination of Subtractive Clustering and Neurofuzzy modeling techniques are used.Three measures are used to validate the adequacy of the models: the mean average percentage errors, the correlation coefficient and the maximum percentage errors.The highest achieved values for these measures are0.5751,0.9999 and 4.7283, respectively.However, although the preliminary findings of the proposed SKAM model are promising, more testing is still required before declaring the adequacy of applying the Softcomputing modeling approach in the project management field.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".