Concurrent Delays in Construction: International Legal Perspective
Bibliographic record
Abstract
The term “concurrent delays” describes the situation when more than one delay occurs simultaneously, either of which would alone delay the overall project. The responsibility of concurrent delays is usually attributable to opposing parties to the contract, such as owner and contractor. This often leads to disputes concerning the extent to which each of the parties is responsible for project delay. Lack of agreement on the approach to properly apportion the damages because of concurrent delays exists throughout the various legal systems worldwide. Most of the time, judgments with respect to apportionment because of concurrent delays by courts are based upon precedents and case law owing to lack of agreed to legal practice. Realizing such need, this study overviews and compares various approaches adopted by courts with respect to ruling on concurrent delay claims and apportionment under different legal system legal systems including the United States (U.S.), Canada, United Kingdom (U.K.), and Australia. This study concludes that in general the U.S. approach in dealing with concurrent delay claims is far more mature and relatively more consistent compared with the other studied legal systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".