A reasoning process in support of integrated project control
Bibliographic record
Abstract
This paper presents a reasoning process that assists members of project teams in performing integrated time and cost control. The process utilizes an object-based model to represent the data structure of a project. A set of resource performance indicators and a factor indicator serves as a group of sensors designed to detect problem-source factors behind unacceptable performance. Problem-source factors and possible corrective actions were identified, making use of the literature and an Internet-based questionnaire. Casual links between earned-value-based variances, performance indicators, problem-source factors, and corrective actions are established. The degree of linkage strength is expressed using fuzzy set theory. Reasons behind unacceptable performance are determined using fuzzy binary relation operations. Possible corrective actions are suggested based on the identified reasons. Three levels of reasoning reports can be generated at individual resource, control-object, and project levels. The reasoning process has been implemented in a prototype system, developed in the World Wide Web. The prototype has an open architecture that allows users to add reasons and actions to those referred to previously. An example project is analyzed to demonstrate the use of the proposed process and illustrate its capabilities. Key words: project control, fuzzy reasoning, progress reporting, diagnose system.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".