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Record W2789795412 · doi:10.1080/21622671.2018.1442245

Ocean grabbing, terraqueous territoriality and social development

2018· article· en· W2789795412 on OpenAlexafffundabout
Paul Foley, Charles Mather

Bibliographic record

VenueTerritory Politics Governance · 2018
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsMemorial University of Newfoundland
FundersSolar Energy Technologies OfficeMemorial University of NewfoundlandAmerican Association of Geographers
KeywordsTerritorialityIndigenousResource (disambiguation)Scale (ratio)State (computer science)PoliticsLand grabbingEconomic geographyPolitical scienceSociologyGeographyEcologyLaw

Abstract

fetched live from OpenAlex

This paper reframes the ocean-grabbing literature by moving beyond accounts where small-scale producers and communities are portrayed as only victims of states and capital. While state and corporate efforts to ‘grab’ resources require critical attention, the literature on ocean grabbing risks obscuring the multidimensional relations of less powerful agents. This paper engages access analysis to reveal complex spatial, social and political processes of inclusion/exclusion and roles of agents such as small-scale producers, trade unions, fishing communities and Indigenous people. Using the case of a circumpolar shrimp species, the paper examines how actors and interests in Canada legitimize access by asserting a form of terraqueous territoriality through claims of adjacency rights – the idea that people living on land contiguous to marine resources ought to have priority in developing these resources. Assertions of terraqueous territoriality enhance opportunities for marginalized groups to gain state endorsement of resource claims, but such assertions are contingent on other factors and progressively tenuous as the mobility and geographical distribution of marine species increases. The paper suggests that contingent ecological and social forces that influence access should receive greater analytical attention, particularly as climate change transforms spatial relations between land-based interests and mobile marine species.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.013
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.206
Teacher spread0.195 · 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 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

Citations84
Published2018
Admission routes3
Has abstractyes

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