Does Tax Knowledge Motivate Tax Compliance in Malaysia?
Bibliographic record
Abstract
This study aims to clarify whether tax knowledge of individual taxpayers motivates tax compliance in Malaysia. Studies with similar topics express the fact that there still exists a gap in profiling the demographic characteristics of knowledgeable taxpayers and better compliant taxpayers in Malaysia. Age, gender, income groups, and education level were the demographic variables used to study the association. The study applied a survey method for data collection. The population targeted was the individual taxpayers across Malaysia, whereby a sample of 419 respondents involved in this study. T-test, One-Way ANOVA, and Pearson correlation analysis had been employed to analyse the data. The outcome of the study reveals that knowledgeable taxpayers are not better tax complaining of taxpayers in Malaysia. Further, the relationship between tax knowledge and tax compliance is negative and insignificant. This paper studied the association of tax knowledge with tax compliance level, which attempt to contribute to the literature and aids tax administration to intensify not only tax law educations but also tax penalties for tax evaders.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".