A knowledge elicitation study to inform the development of a consequence model for Arctic ship evacuations: Qualitative and quantitative data
Bibliographic record
Abstract
Expert knowledge was elicited to develop a life-safety consequence severity model for Arctic ship evacuations (Browne et al., 2021). This paper presents the associated experimental design and data. Through semi-structured interviews, participants identified factors that influence consequence severity. Through a survey, participants evaluated consequence severity of different ship evacuation scenarios. The methodology represents a two-phased mixed methods design. Life-safety consequence severity is measured as the expected number of fatalities resulting from an evacuation. Participants of the study were experts in various fields of the Arctic maritime industry. Sixteen experts participated in the interviews and the survey (sample size: n = 16). Sample size for the interviews was based on thematic data saturation. Predominantly the same group of experts participated in the survey. Interviews were analysed using thematic analysis. Interview data informed the development of evacuation scenarios defined in the survey. The interview guide and survey questions are presented. Data tables present the codes that emerged through thematic analysis, including code reference counts and code intersection counts. Data tables present the raw data of participant responses to the survey. This data can support further investigation of factors that influence consequence severity, definition of a broader range of evacuation scenarios, and establishment of associated consequence severities. This data has value to Arctic maritime policy-makers, researchers, and other stakeholders engaged in maritime operational risk management.
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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 | 0.002 |
| 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 | 0.000 |
| 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".