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Record W3159093684 · doi:10.29173/psur231

Examining the CPEC through the Lens of South-South Cooperation

2021· article· en· W3159093684 on OpenAlexaffvenue
Lochlann Kerr

Bibliographic record

VenuePolitical Science Undergraduate Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBlueprintChinaConditionalityPerspective (graphical)Political scienceSouth–South cooperationEconomic growthGeographyRegional scienceBusinessEconomicsEngineeringPoliticsComputer science

Abstract

fetched live from OpenAlex

Worth over $62 billion USD, the China-Pakistan Economic Corridor (CPEC) is one of the largest infrastructure partnerships between two developing states in the world and is lauded by China and Pakistan as a model of development cooperation. By viewing the program from the perspective of a South-South Cooperation (SSC) framework, this paper analyzes the extent to which the CPEC operationalizes the principles of the SSC paradigm. In examining the CPEC’s relation with the standards of mutual benefit, equality, non-interference, and non-conditionality, China and Pakistan’s claims that the program is a blueprint for South to South development cooperation is tested by this research.

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)0.004
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.010
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.003
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 head0.109
GPT teacher head0.363
Teacher spread0.253 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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