Application of Social Network Analysis for Analyzing the Relationships between Root and Direct Causes of Defects
Bibliographic record
Abstract
<p class="zhengwen">This paper addresses the application of social network analysis (SNA) in understanding and representing the relationships between the root and direct causes of defects. The root and direct causes of construction defects were identified through extensive literature review, and the thoroughness of the identified causes was confirmed by examining 91 non-conformance reports. The SNA software UCINET was used to visually map the links between the direct and root causes for identifying the root causes that accounted for the majority of direct causes and defects. A measure of centrality and adjacency indicated that the root cause Constructor Error/Omission was directly linked to seven of the ten identified direct causes. It was also determined that eliminating this root cause together with Transportation Error would reduce the number of defects by 90%. Since the root causes responsible for the majority of direct causes as well as the largest number of defects could be identified using SNA, it is concluded that SNA is a valuable tool for recognizing where resources should be employed for the elimination of defects.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".