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Anti-tax Avoidance of Multinational Corporations under the Belt and Road Initiative in China

2025· article· en· W4406035433 on OpenAlexaffabout
Cuiyi Wu

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMacEwan University
Fundersnot available
KeywordsTax avoidanceMultinational corporationBusinessTax revenueTax reformChinaInternational taxationDouble taxationCorporate taxContext (archaeology)Public economicsEconomicsFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

In the context of expanding economic globalization, the numbers of the Multinational Corporation (MNC) have been increasing. “One Belt One Road” promotes the trade corporation between countries. In light of the MNCs implement various tax avoidance strategies, this has significant adverse impact on business environment and tax revenue. This article mainly relies on literature research and comparison of anti-tax avoidance Legal Framework between China and Canada. It explores different measures of anti-tax avoidance. China has experienced challenges with tax avoidance issue in the BRI, including lack of competitiveness of China’s Tax Rate, complexity of international taxation environment, and challenges of implementing Organisation for Economic Co-operation and Development (OECD) Pillar II Global Minimum Tax. Furthermore, comparing the anti-tax avoidance legal framework and differences in its GAAR respectively. The purpose is to design a tax system with Chinese characteristics. Effective anti-tax measures is very crucial for ensuring fair taxation and foster competitive business environment, and increase the public trust for China.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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 teacher head, 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
Published2025
Admission routes2
Has abstractyes

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