Putting on a bow-tie to sort out who does what and why in the complex arena of marine policy and management
Bibliographic record
Abstract
Marine policy and management has to cope with a plethora of human activities that cause pressures leading to changes to the natural and human systems. Accordingly, it requires many policy and management responses to address traditional, cultural, social, ecological, technical, and economic policy objectives. Because of this, we advocate that a fully-structured approach using the IEC/ISO 31010 Bow-tie analysis will allow all elements to be integrated for a cost-effective system. This industry-standard system, described here with examples for the marine environment, will fulfil many of the demands by the users and uses of the marine system and the regulators of those users and uses. It allows for bridging several aspects: the management and environmental sciences, the management complexity and governance demands, the natural and social sciences and socio-economics and outcomes. Most importantly, the use of the Bow-tie approach bridges systems analysis and ecosystem complexity. At a time when scientific decisions in policy making and implementation are under question, we conclude that it provides a rigorous, transparent and defendable system of decision-making.
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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 | 0.049 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.040 | 0.064 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".