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Record W4414092540 · doi:10.1080/09692290.2025.2553557

The emerging political economy of deep-sea mining: an analysis of opaque ownership structures

2025· article· en· W4414092540 on OpenAlexafffund
Justin Alger, Kate J. Neville, Marc Calabretta

Bibliographic record

VenueReview of International Political Economy · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPoliticsInternational political economyCorporate governanceEmerging marketsGlobalizationIdeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.283
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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