Improving decision quality: a risk-based go/no-go decision for buildoperatetransfer (BOT) projects
Bibliographic record
Abstract
The buildoperatetransfer (BOT) mechanism is used worldwide to promote diverse infrastructure projects. Success in BOT projects mainly depends on selecting the right project to promote. The right project selection initiates from identifying a presumably viable project to pursue at the early project initiation process. When deciding on a prospective project to pursue, developers in many cases rely on their judgment, intuition, or rules of thumb rather than analytic evaluation of the complex BOT characteristics and specific project conditions. It is expected that they will improve the quality of their decisions if a methodical formalism is provided that can help systematically recognize (i) risk factors in the BOT project environment, (ii) the impact of these decisions on project feasibility, and (iii) strategic alternatives to enhance these decisions. The main objective of this research is to develop a risk-based, go/no-go decision model as the formalism, which consists of a decision process model and a decision variables relationship model. A numerical example is presented to demonstrate the computational procedures of the model. In an effort to validate the model, this research invites 60 test subjects and adopts convergent experimental studies.Key words: buildoperatetransfer, go/no-go decision, decision quality, multi-attribute decision-making, convergent validation.
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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 | 0.029 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".