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Record W3194960643 · doi:10.26443/firr.v9i1.8

Fishery Depletion and the South China Sea

2019· article· en· W3194960643 on OpenAlexvenueno aff
Jaymes MacKinnon

Bibliographic record

VenueFlux International Relations Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsnot available
Fundersnot available
KeywordsOverexploitationMilitarizationChinaFisheryGeographyTragedy of the commonsDevelopment economicsPolitical scienceCommonsEconomicsBiology

Abstract

fetched live from OpenAlex

Fishery depletion is a driving force in the militarization of the South China Sea. Using Garrett Hardin’s theory “the tragedy of the commons” as an analytical lens, this paper explores the relationship between the lack of legitimate territory designations and the illegal overexploitation of wild fish stocks. It argues that China, as the regional hegemon, has triggered conflicts by pursuing an agenda of maritime territorial expansionism. Some Southeast Asian countries, affected by these resource-driven incursions, defend their exclusive economic zones through military buildup. Therefore, the rising violence and decreasing availability of fish force some non-commercial fishermen to pursue piracy as an alternate form of income. The findings of this paper suggest that increased militarism of the South China Sea has not only predominantly affected the lives of non-commercial fishermen but also negatively impacted the regional environmental health. In the future, without multilateral resource management, this militarization will only worsen.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.271
Teacher spread0.260 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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