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Record W2781915522 · doi:10.1177/1748048517742783

The political economy of Chinese internet companies: Financialization, concentration, and capitalization

2018· article· en· W2781915522 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Communication Gazette · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsCarleton UniversityYork University
Fundersnot available
KeywordsThe InternetChinaNexus (standard)BusinessCapital (architecture)FinancializationCapitalizationGovernment (linguistics)PoliticsMarket economyFinanceFinancial systemEconomicsPolitical science

Abstract

fetched live from OpenAlex

The rapid growth of the internet in China has been propelled by the Chinese government’s push to develop the country’s information infrastructure and its tight control over the internet. The most recent stage of internet development in China, however, has been driven by a three-way dynamic between the State, internet companies, and international finance capital. This relationship has yielded three internet giants—Baidu, Alibaba, and Tencent—that stand at the apex of the internet economy in China. They also rival their US counterparts like Google, Facebook, and Amazon, on several key measures. We examine annual reports and other financial documents to better understand these three companies’ character as ‘capitalist enterprises’ and the tight nexus that links them to international investment banks, venture capital funds, and other foreign investors. While these processes are now fundamentally shaping ‘the Chinese internet’, they have not yet been adequately explored in the scholarly literature, we argue.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.300
Teacher spread0.290 · 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