D4.1 deliverable:Developing the Multi-layered Collaborative Marine Governance Model
Bibliographic record
Abstract
This deliverable presents the Multi-layered Collaborative Marine Governance model (MLCMG model). It is an analytical model that brings together different components to understand the enabling and constraining conditions for a successful implementation of the EU Green Deal (GD) objectives for the 4 PERMAGOV regime complexes (Maritime Transport, Marine Life, Marine Energy and Marine Plastics). The MLCMG model, together with the indicators for assessing governance capabilities (WP5), will result in PERMAGOV’s Marine Governance Performance Assessment Framework. The MLCMG model is developed from existing social scientific approaches and theories (including Sociology, Political Science, Public Policy and Governance, and Planning) and includes several components that affect change, innovation, and performance of marine governance arrangements: • the institutional context/setting and structural conditions affecting collaborative processes; • the multi-level dynamics of marine decision making and implementation of marine policies; • marine governance arrangements (comprising actors/coalitions, rules of the game, resources and discourses); • the characteristics of the collaborative process in which governmental actors (public agencies) in deliberation with non-state actors (e.g., representatives of maritime sectors, non-governmental organizations) are engaged in decision-making of marine policies; • the governance capabilities of state and non-state actors to attain societal goals; and • the role of e-governance to enable the effective implementation of the Green Deal. In practical terms for implementing the EU GD, the MLCMG model offers a simplified representation of how marine governance arrangements change and innovate over time. We conceive marine decision-making and implementation of marine policies as a collaborative process structured by institutions, constrained by institutional barriers, enabled by governance capabilities of actors, and facilitated by marine e-governance. We consider performance as the capacity of marine governance arrangements to solve societal problems and create societal opportunities.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.032 |
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".