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Record W4407591019 · doi:10.1007/978-3-031-71322-4_2

Overdetermined by Territory? Governing the Ocean in Time, Matter, and Rhythm

2025· book-chapter· en· W4407591019 on OpenAlexaff
Christopher McAteer

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsYork University
Fundersnot available
KeywordsOverdetermined systemRhythmOceanographyGeologyEnvironmental scienceMathematicsPhysicsMathematical analysisAcoustics

Abstract

fetched live from OpenAlex

Abstract This chapter asks whether thinking about space goes far enough in comprehending the ocean, arguing that scholars must engage more with materiality and temporality to discover new forms of ocean governance. Noting the important scholarly work that has moved beyond a planar, or areal, conceptualisation of the ocean, the chapter draws on recent scholarship to critically assess volumetric theorisations that do not sufficiently account for the fluidity of matter within the ocean. It contends that scholars must think through the complex materiality of the ocean by conceptualising the highly mobile stuff within it as being an inherent part of ocean space itself. It then argues that to think about the materiality of the ocean and how it moves and flows, one must engage with temporality, helping one to move beyond both two-dimensional thinking and three-dimensional volumetrics towards an understanding of the ocean as processual and something that happens in time. Surveying recent literature on relational approaches to space, the chapter argues that the future of ocean governance must be multiple, complex, and messy, rather than overdetermined by a political logic of territory that reifies state borders as a byproduct of its legal mechanisms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.011
Scholarly communication0.0050.005
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.003
GPT teacher head0.164
Teacher spread0.161 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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