Bibliographic record
Abstract
Goods and Services Tax (GST) is a value-added indirect tax at each stage of the supply of goods and services precisely on the amount of value addition achieved. It seeks to eliminate inefficiencies in the tax system that result in ‗tax on tax‘, known as cascading of taxes. GST is a destination-based tax on consumption, as per which the state‘s share of taxes on inter-state commerce goes to the one that is home to the final consumer, rather than to the exporting state. GST has two equal components of central and state GST. France was the first country to implement the GST in 1954, and since then an estimated 160 countries have adopted this tax system in some form or another. Some of the countries with GST include Canada, Vietnam, Australia, Singapore, the U.K., Monaco, Spain, Italy, Nigeria, Brazil, and South Korea. India joined the GST group on July 1, 2017. Most countries with a GST have a single unified GST system, which means that a single tax rate is applied throughout the country. A country with a unified GST platform merges central taxes (e.g. sales tax, excise duty tax, and service tax) with state-level taxes (e.g. entertainment tax, entry tax, transfer tax, sin tax, and luxury tax) and collects them as one single tax. These countries tax virtually everything at a single rate. In this paper an attempt is made to introduce the concept of Goods and service Tax and briefly discuss the impact of GST in Indian economy .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".