Improving Concurrency Assessment and Resolving Misconceptions about But-For Delay Analysis Technique
Bibliographic record
Abstract
But-for delay analysis is a popular technique used in practice and accepted by arbitration boards and courts. However, misconceptions are common when the analysis results are interpreted from different parties’ viewpoints. In addition, the adoption of either the literal or functional views on concurrent delays affects the results. This paper thus clarifies the misleading interpretations of but-for results and introduces improvements and an explicit implementation procedure that matches the delay analysis requirements of professional bodies such as the Association for the Advancement of Cost Engineering International (AACEI). To more accurately perform but-for analysis considering all parties’ viewpoints, the paper uses Venn representation and suggests a simplified procedure to check for true concurrency. A case study was used to show a detailed procedure for applying but-for with multiple analysis windows as a more accurate approach to assess concurrent delays and to consider baseline updates. The applicability of the proposed improvements was then confirmed using a second practical case study. The paper is expected to remove the existing but-for misconception and provide a procedure for more accurate and repeatable delay analysis considering concurrent delays.
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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 | 0.030 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".