Managing the Cost of Power Transmission Projects: Lessons Learned
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
A major driver to project success is the ability to manage the project cost effectively. Despite the agreement among scholars and practitioners on the importance of managing the project cost, excessive cost overruns continue to occur on major power transmission projects. In this paper, the author, through a Delphi method, will discover problems of managing the project cost, suggest solutions to overcome these problems, and identify lessons learned from these projects. The study was conducted with two different project teams in the same organization in Canada. Key findings from this study will highlight similarities and differences between the two cases in terms of how each team managed the project cost and learned from it. The paper will contribute to the body of knowledge by identifying lessons learned from power transmission projects on how to manage the project cost and by suggesting solutions to overcome the problem of cost overrun in these projects.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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