MétaCan
Menu
Back to cohort
Record W1547912301

E-Commerce Tax Policy in Australia, Canada and the United States

2000· article· en· W1547912301 on OpenAlexaffabout
Jinyan Li

Bibliographic record

VenueeYLS (Yale Law School) · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsYork University
FundersU.S. Department of the Treasury
KeywordsScope (computer science)Tax policyPosition (finance)Consumption taxTax reformPublic economicsState income taxTax avoidanceValue-added taxAd valorem taxInternational taxationEconomicsDouble taxationConsumption (sociology)International economicsIncome taxBusinessInternational tradeFinance
DOInot available

Abstract

fetched live from OpenAlex

This article provides a brief overview of the major international tax problems created by e-commerce and the policy position of Australia, Canada, the United States, and the OECD. The discussion is limited to income tax issues. Although e-commerce also raises challenges for consumption tax, this issue is beyond the scope of this article.

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)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0090.004
Scholarly communication0.0090.002
Open science0.0010.002
Research integrity0.0030.003
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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
GenreOther

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

Citations3
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueeYLS (Yale Law School)Same topicCorporate Taxation and AvoidanceFrench-language works237,207