Bibliographic record
Abstract
India is witnessing one of the largest indirect tax reforms since independence in the form of goods and services tax , which was implemented from July 1, 2017 . But here one question that strikes the minds of the layman , i.e. , a person who does not have knowledge about the technicalities of taxation and it's structure is why such a change in the taxation system is required. Now when everything is settled and indirect tax collections are also positive , then why to change the tax system . When everything is going smooth and they are not facing any problem they will certainly resist the change. Developed countries adopted GST to increase revenue from general consumption , to cut down rate of income taxes , to consolidate and modernize their existing tax structure. It would be beneficial to look at the international scenario to understand the various GST models in vogue in certain countries. This paper focus on the various loopholes in the current indirect tax structure such as multiplicity of taxes , cascading effect , classification issues etc. and compares the economy of developed countries namely Canada , Singapore , Malaysia with India . It outcomes that GST is a step that will address the issues of the current indirect tax regime and resolve the same for the benefit of the tax payer
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".