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Record W188083430

Designing Tax Policy for the Digital Biosphere: How the Internet is Changing Tax Laws

2002· article· en· W188083430 on OpenAlexaff
Arthur J. Cockfield

Bibliographic record

VenueSSRN Electronic Journal · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsQueen's University
Fundersnot available
KeywordsTax reformLegal aspects of computingBusinessThe InternetCyberspaceTax avoidanceAd valorem taxValue-added taxDirect taxLaw and economicsLawEconomicsPublic economicsPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

In order to promote a better understanding of the relationship between tax laws and Internet transactions, this Article integrates tax policy discussion with cyberlaw theory by developing a theoretical legal model called the biosphere. The digital biosphere is the Internet, an online forum characterized by internal diversity and technological evolution where commercial and non-commercial activities overlap to a certain extent. The role of tax law, it is argued, is to protect real world norms (e.g., the desire to maintain neutral tax treatment between traditional commerce and e-commerce) by aligning tax laws with the nature of the network without unduly interfering with emerging cyberspace norms (e.g., transactional anonymity). On the one hand, reform efforts such as the state-sponsored Streamlined Sales Tax Project (SSTP) are properly addressing the challenges presented by Internet commerce by advocating an Internet-based automated sales and use tax collection system, the extension of state and local tax jurisdiction over remote e-commerce vendors, and the unification of different state and local tax bases. On the other hand, other reform efforts such as the new OECD rule to tax computer server/permanent establishment profits for international income tax purposes fails to address these challenges.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.212
Teacher spread0.192 · 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.

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

Citations19
Published2002
Admission routes1
Has abstractyes

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