Cohen’s Weighted Kappa Statistic in Quality Control–Quality Assurance Procedures: Application to Network-Level Contract Pavement Surface Condition Surveys in British Columbia, Canada
Bibliographic record
Abstract
Like many provincial and municipal agencies, the British Columbia Ministry of Transportation (BCMoT) contracts out the collection of pavement surface condition data. Because BCMoT is committed to contracts with multiple private contractors, quality assurance (QA) plays a critical role in ensuring that the data are collected accurately and repeatably from year to year. Comprehensive QA testing procedures for surface distress data have been developed and implemented since the data collection has been based on visual ratings with event boards. Control sites that are manually surveyed are used to evaluate whether the contractor is correctly applying the BCMoT pavement surface distress rating system. To date, the QA testing has been based on a composite-index–based criterion for assessing the level of agreement and supplemented with the detailed severity and density rating data. However, the use of a composite index presents some limitations related to the model formulation and weightings assigned to particular distress types. Although the detailed ratings are useful as a diagnostic tool to pinpoint discrepancies, in the disaggregated format, they are not conducive as acceptance criteria for QA testing. Not widely used in the field of engineering, Cohen’s weighted kappa statistic has been applied since the 1960s in other areas to assess the level of agreement beyond chance among raters. The statistic was therefore identified as a possible solution for improving the ministry’s QA surface distress testing process by providing an overall measure of the level of agreement between the detailed manual benchmark survey and the contractor severity and density ratings. The application is described of Cohen’s weighted kappa statistic for visual surface distress survey QA testing using the BCMoT survey and testing procedures as a case study.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".