Engineer participation in scheduling and budgeting: the effect on project performance
Bibliographic record
Abstract
Summary form only given, as follows. The author looks at how engineer participation in the budgeting and scheduling of a research and development project affects the project's performance in these areas. Data were collected at a Federally Funded Research and Development Center (FFRDC). The FFRDC where the data were collected is involved in some projects which were of an exploratory nature and thus involve high levels of technical uncertainty. For this study, six projects were selected which span the range of projects from those with very little technical uncertainty to those with very high levels of technical uncertainty. The engineers on the six projects were surveyed regarding their participation in project budgeting and scheduling (response rate was 80.6 percent). The division leader for the division where the projects were housed rated each product's performance on budget and schedule as well as the project's overall performance. It was found that no correlation exists between engineer participation in project scheduling and budgeting and project schedule and budget performance. Further exploration via interviews found that it was how the inherent technical uncertainty was handled that seemed to drive project success.>
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".