Regular papers / Articles ordinairesReceiver operating characteristic (ROC) curve analysis of the effectiveness of construction planning efforts in Australia and the United Kingdom
Bibliographic record
Abstract
This paper presents the results of a receiver operating characteristic (ROC) curve analysis of the effectiveness of construction planning efforts, based on samples of building projects in Australia and the United Kingdom. The results of the study provide an evaluation of the extent of the effort that must be invested in planning and control activities to achieve success in the performance of construction projects. The study also illustrates the potential application of ROC curve analysis in construction engineering and management research. Planning efforts in a sample of 52 building projects in Australia and 37 building projects in the United Kingdom were evaluated and compared. This study builds on work done in an earlier study in which the concept of optimal planning of construction projects was explored. The ROC curve analysis offers several advantages over the regression methodology employed in the previous optimal planning study. The graphical representation of the relationship between sensitivity and specificity over all possible diagnostic cutoff points provides an insight into the interactions of the variables that was not apparent in the original methodology.Key words: construction planning, project planning, project management, ROC curve analysis, Australia, United Kingdom.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".