Factor analysis of the interface management (IM) problems for construction projects in Alberta
Bibliographic record
Abstract
Interface management (IM) is one of the major keys for construction project success. The severity of interface problems for different projects does not only delay the project, but also impacts overall project performance. This paper is an extension of a previous work that defined major IM problems in Alberta’s construction projects. This research study intended to investigate, identify, and classify interface problem factors in Alberta’s construction projects. The study included four stages. The first stage was a comprehensive literature review, pilot studies and face-to-face interviews in industry. In the second phase, a web-page questionnaire was conducted with participants from industry. Based on that, in the last two phases, a factor analysis and Pearson’s correlation matrix were applied on the collected data. The study identified six IM factors, namely: “management”, “information, bidding and contracting”, “by-law and regulation”, “technical engineering and site issues”, and “other interface problems”. Finally, correlation between IM factors and different construction data was tested. The data analysis results provided a comprehensive view of the main causes behind IM conflicts in Alberta’s construction industry.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".