Risk maturity model for maritime authorities: testing the internal consistency and inter-rater reliability of the R-Mare matrix
Bibliographic record
Abstract
Abstract The R-Mare matrix is the first risk maturity model developed for maritime authorities. Its main purpose is to support the authorities in self-evaluating their current risk management performance and in steering that performance toward higher levels. To gain insight into the model’s usability in practical applications, the aim of this study is to test the reliability of its measurements in real-world settings and identify areas for improvement. The empirical testing is conducted within maritime administration in the Baltic Sea region and involves two rounds of semi-structured interviews with 16 panelists. During these interviews, the panelists employ the R-Mare matrix model to provide individual ratings of their administration’s risk management performance and justify their expert judgments. To achieve a comprehensive understanding of the model’s reliability, the collected data is analyzed using internal consistency reliability and inter-rater reliability tests. For the former, statistical quantifications are performed using Cronbach’s Alpha and McDonald’s Omega coefficients, while for the latter, the Intraclass Correlation Coefficient is employed. These tests are conducted separately for the results of both interview rounds to further address the model’s temporal stability. The findings indicate a high degree of internal consistency reliability, and at least a moderate degree of inter-rater reliability for the model’s measurements. These further highlight areas for improvement in the model and assist in developing strategies to enhance its reliability. Consequently, the results provide evidence of the reliability of the R-Mare matrix model’s measurements, while supporting its deployment in the maritime administrations of the Baltic Sea region and beyond.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".