Framework for Optimizing Team Performance and Project NPV: Enhancing the Probability of Success by Team Alignment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study addresses a gap in enterprise risk management related to project team performance. Poorly functioning teams may severely erode project net present value (NPV). The erosion of project NPV can be quantified in terms of probability of success (POS). In the oil business POS is based on success criteria for likelihood that exploration efforts for oil & gas prospects will realize the EMV for those assets. Similarly, POS in team work and negotiations is based on success criteria for the likelihood that cooperation between team individuals will be able to deliver the maximum value for the project. Practical rules are formulated to support teams and team leaders in their efforts to optimize the alignment of team members in order to enhance the team’s effectiveness. The probability of success (POS) is split into three fundamental factors of alignment: PCulture , PSkills and PGoals. The dynamic effect of team learning on team alignment is graphed as the Cumulative POS. The cost of failure is graphed for a range of POS values, and visualizes the impact on the EMV of extra Team OPEX, each normalized by the project NPV. Applications are possible in all kinds of functional teams, including change management teams that need to build coalitions to effectuate lasting change. The interaction between members of engineering and other professional teams has been studied intensively, but the expression of team performance in numbers as quantified here is a new direction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it