Towards Practical Definitions of Quality of Maritime Risk Analyses During Procurement Processes
Bibliographic record
Abstract
In the maritime context, national authorities and other actors regularly procure risk analyses from external providers.In the public sector this requires the drafting, publishing and evaluating the outcomes of formal calls for tenders.In such procurement processes, the quality of the received proposals is typically highlighted as a key criterion to be used when deciding on the winning bid, alongside other features such as price and the availability of sufficient personnel/other resources.This implies estimating the quality of a risk analysis, before it is carried out.As this is naturally a challenging task, the quality criteria of risk analyses are commonly simplified one way or another, often involving the perceived quality of previously produced studies or simply relying on the provider's overall reputation.This might be convenient in practical situations where a provider must be selected under time pressure.However, it may present a missed opportunity to ensure best value for money and, in the bigger picture, raise the standard of commissioned risk studies and the field at large.Our contribution builds on the SRA Risk Analysis Quality Test with a specific focus on which tests could be relevant for the risk analysis tendering stage.Based on an initial review by the authors, we propose two lists of key criteria for this purpose: one for drafting calls for proposals and another for evaluating them.Aimed primarily to initiate a focus on this aspect of risk management, the initial lists will be further developed during interviews and workshops with potential end-users in future work.
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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 | 0.332 | 0.460 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.035 | 0.036 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".