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Record W2976097564

Taxation of Copyright Royalties in India - Interplay of Copyright Law and Income Tax

2019· article· en· W2976097564 on OpenAlexaboutno aff
Ganesh Rajgopalan

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentTax lawConventionIncome taxRelevance (law)JurisprudenceInternational taxationLaw and economicsLawDouble taxationIntellectual propertyEconomicsPolitical scienceBusinessTax reformFinance
DOInot available

Abstract

fetched live from OpenAlex

The book is a thematic commentary on the law relating to taxation of royalty payments in respect of copyright both under the Income-tax Act, 1961 and tax treaties. The book deals with payments for transfer of rights in respect of copyright and for the use of copyright, to give a sharply focussed view on the subject. The book dives deep into the various facets of copyright law as well as analyses the differences in copyright laws of various countries and their impact on income taxation. The book discusses the rich jurisprudence from the United States, the United Kingdom, Poland, Canada, and other countries and examines their relevance for taxation. The commentary first explores the concepts of nature of copyright; the varying consequences of ownership, licence, and assignment of copyrighted works; copyright royalty under tax laws and treaties before delving into the specific scenarios that arise in case of software, cinematographic films, broadcasts, and databases. The author has also addressed questions of law owing to digitisation and the internet. The controversies surrounding characterisation of software payments made by the end users and the resellers are analysed. The book analyses the changes made to the OECD Model Convention Commentary on Article 12 on Royalties in respect of software payments. Published by Oakbridge Publishing P Ltd (2019).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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