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Record W234506804

Raise Management Standards of Property Tax on Transnational Corporations ELEVER LE NIVEAU DE GESTION FISCALE A L'EGARD DES ENTREPRISES TRANSNATIONALES

2006· article· fr· W234506804 on OpenAlexvenueno aff
Cui Yinghui, Juan Wei, Hui Chen

Bibliographic record

VenueCanadian social science · 2006
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicEconomic Zones and Regional Development
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationChinaBusinessRevenueTax revenueTransfer pricingDutyEconomicsInternational economicsMarket economyPublic economicsFinancePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

As China joined WTO, more and more foreign multinational companies will enter China. They run enterprises within the territory of China. This will inevitably play an active role in promoting the economic construction of China. But we should pay attention to some foreign multinational companies in China that often reduce the incomes of Chinese branch companies by utilizing transfer prices. Thus they achieve the goal of evading payment of duty. These make the tax revenue of Chinese Government fall sharply. So it is very important to raise management level of property tax to the multinational companies. This text explains the loss caused by the foreign businessmen using improper means to transfer prices. They import raw materials at a high price and export the products at a low price. Also the corresponding measures are made out in this paper.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.024
GPT teacher head0.207
Teacher spread0.183 · 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 designNot applicable
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
Published2006
Admission routes1
Has abstractyes

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