Taxpayers' acceptance level of goods and services tax (GST) / Nur Syazwani Mohammad Fadzillah and Zuhariah Husin
Bibliographic record
Abstract
Generally, tax is recognized as one of the main sources of government's revenue and Goods and Services Tax (GST) is an example of tax that contributes to it. This tax has been implemented in many countries such as Canada, Australia, and New Zealand. Roughly, 90 percent of the world's population lives in countries with GST. In Malaysia, GST was implemented on 1 April 2015 at 6% rate and it replaced the present consumption tax comprising the sales tax and the service tax. The issue on GST has been raised by the Malaysian Government as an approach to reduce its deficit. GST is imposed on goods and services throughout production-distribution stages in the supply chain including importation of goods and services. The tax is embedded in the price of goods and services transacted. However, the implementation of GST in Malaysia has called many arguments from various parties including academics, professionals and the taxpayers on how GST affects goods prices, either increase or decrease. The consumers are worried about the significant price increases on basic needs. With the relatively-high living costs, significant price increases due to GST is considered as another burden for the taxpayers. Therefore, the main objective of this study is to investigate the level of acceptance of taxpayers regarding GST implementation. This study utilized survey questionnaires distributed to lecturers of Universiti Teknologi MARA (UiTM) Pahang. The findings hopefully will shed a clearer view on the taxpayers' acceptance level of GST to the tax authorities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".