D4.2 Validation of the MLCMG model:Report of the validation and refinement of the Multi-layered Collaborative Marine Governance Model for each of the cases.
Bibliographic record
Abstract
This deliverable presents the validation and refinement of the Multi-layered Collaborative Marine Governance model (MLCMG model) for the 9 PERMAGOV cases in the 4 regime complexes (Maritime Transport, Marine Life, Marine Energy and Marine Plastics). In an interactive and participatory way, the case study leads together with the end-users coordinated the validation and refinement of the analytical model through case-specific data collection strategies and validation panels. The refinement of the model will form the basis for the assessment in WP5, the development of Multi-level Collaborative Marine Governance Strategies in WP6 and communication materials in WP7. The first six chapters comprise the main report. In here, the report describes the purpose of the deliverable, gives an explanation of the MLCMG model, provides an overview of the methodology of the case study reports, discusses the validation and refinement of the model, and ends with a conclusion. The nine case studies subsequently are added as annexes to the report. The MLCMG model developed in task 4.1 structured the case study reports. The main part of each of the case study reports describes how marine governance arrangements are changing and innovating. In addition, each of the case studies have identified relevant linkages between the model sub-components, provided an overview of the main governance challenges, and reflected on the use value of the MLCMG model to conduct case study analysis. The D4.2 report concludes with a general interview model for the MLCMG model.
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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 | 0.055 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".