Marine Governance as a Process of Reflexive Institutionalization? Illustrated by Arctic Shipping
Bibliographic record
Abstract
Abstract The objective of this chapter is to give insight in marine governance challenges, illustrated by Arctic shipping. To do this, this chapter presents a theory of marine governance as reflexive institutionalization, in which the structural properties of marine governance arrangements are (re)produced in interactions between governmental actors, maritime sectors and civil society actors within the structural conditions of the networked polity at sea. Based on an analysis of the institutionalization of shipping governance arrangements of three (possible) Arctic shipping routes; The Northwest Passage (NWP), the Northeast Passage and Northern Sea Route (NEP/NSR), and the Transpolar Sea Route (TSR) the following question will be answered, “What are the enabling and constraining conditions of marine governance as reflexive institutionalization?” In other words, what are the possibilities for public and private actors to challenge discursive spaces and to change the rules of the game, in order to find solutions for environmental, spatial, economic, and social problems at the Arctic Ocean? The analysis shows forms of institutionalization as structural reflectiveness in which the dominant discourse ‘shipping is allowed in the Arctic’ is not challenged. However, this form of reflectiveness showed how actors, such as China and Russia, are able the use rules from different institutional settings to strengthen their position.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".