The emerging political economy of deep-sea mining: an analysis of opaque ownership structures
Bibliographic record
Abstract
Despite the significant environmental and social concerns associated with deep-sea mining, the International Seabed Authority (ISA)—the organization tasked with governing the international seabed—is working to finalize a mining code. State and private sector proponents justify these developments as necessary to expand renewable energy and battery production, but this claim remains contested. Under international rules, commercial actors—whether private or state-owned—can hold exploration licenses in tandem with sponsoring states, yet we know little about how corporate actors are shaping governance of the sector. It is difficult to disentangle the positions of states from those of private firms and market actors, and there remain significant gaps in our knowledge of how ownership and finance in the sector are structured and organized. We argue that deep-sea mining represents a paradigmatic case of growing distance in environmental governance. In this case, the capacity of international actors to prevent or effectively regulate the ecological and social harms of deep-sea mining is challenged by the opacity and mutability of corporate structures. The implications, we suggest, are twofold: the redistribution of risk to states and local communities, and the undermining of the common heritage of humankind principle embedded in the UN Convention on the Law of the Sea.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".