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Record W3011752700 · doi:10.26524/jms.2017.34

A comparative study of indian gst with canada model

2017· article· en· W3011752700 on OpenAlexaboutno aff
Vijaya Kumar K, C JAHAMGEER

Bibliographic record

VenueJournal of Management and Science · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDual (grammatical number)Government (linguistics)Service (business)Indirect taxTax evasionState (computer science)Goods and servicesEconomicsBusinessEconomyTax reformPublic economicsComputer science

Abstract

fetched live from OpenAlex

The newly implemented Goods and Service Tax (GST) system of India willbring 'One Nation One Tax' to unite the existing Indirect Taxes under one umbrella. Theimportant motive of the government to enact the GST was to make our business Globallycompetitive, Efficient Tax collection, Easy inter-state movement of goods, Reduction in thecorruption, Removing of cascading effect of tax, Higher threshold for registration, Onlinesimpler procedures etc.. Goods and service tax is taking India by the storm. India has chosenthe Canadian model of dual GST. France was the first country to implement GST to reducetax-evasion. Since then, more than 140 countries have implemented GST with some countrieshaving Dual-GST, for example Brazil and Canada. India has chosen the Canadian model ofdual GST as it has a federal structure where the Centre and states have the powers to levy andcollect taxes. This paper especially focused on Comparative Study of Indian GST withCanada Model.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.207
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.061
GPT teacher head0.248
Teacher spread0.187 · 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
Published2017
Admission routes1
Has abstractyes

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