MétaCan
Menu
Back to cohort
Record W7115034364

STRATEGIC STALEMATES: Explaining Gray Zone Assertions of China in the South China Sea

2025· dissertation· en· W7115034364 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsnot available
FundersMcGill University
KeywordsGray (unit)ChinaChina seaMediterranean sea
DOInot available

Abstract

fetched live from OpenAlex

The South China Sea is one of the world’s most resource rich and geo-strategically important regions. Therefore, it is not a surprise that China has sought expansionist claims in this region to increase its power through its controversial nine-dash line. These assertions directly clash with the sovereign claims of several other littoral states in the region, namely Indonesia, Malaysia, Vietnam, the Philippines, Taiwan and Brunei. China remains unyielding in maritime disputes in the East and South China Seas, underscoring its strict stance on these strategically valuable regions. This thesis attempts to understand why these claimant states in the South China Sea are showing no signs of explicit balancing or alliance building against rising Chinese aggression in the region. I propose that this behavior might be explained by a “two layered gray zone approach” adopted by China. This approach includes economic coercion (through a “strings attached” investment model) and covert/hybrid aggression (through armed civilians, maritime militias and coast guards). These strategies make China less accountable to the international community and essentially binds these States to be economically linked with Beijing with no viable strategies to escalate militarily due to the civilian component of aggression creating an asymmetry between contesting states and China. The thesis will test this theory on four key claimant states in the region- Vietnam, the Philippines, Indonesia and Malaysia. The thesis will explore whether cases with a higher level of 'elite capture'—i.e., a more dependent elite network with Beijing—display a lower level of aggression in the South China 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 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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.016
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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 designQualitative
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 routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicInternational Maritime Law IssuesFrench-language works237,207