The Impact of Tax Governance on the Governmental Corruption level in Jordan
Bibliographic record
Abstract
The current study aimed at identifying the impact of applying two tax governance mechanisms (i.e. direct and indirect tax control) on the financial and administrative governmental corruption levels at the income and sales tax department in Jordan in 2017. The researchers developed a questionnaire to collect data. Then, they distributed the questionnaire forms to the random sample they selected. The study’s sample consists of 22 tax auditors, 8 tax supervisors, and 2 heads of the auditing departments who work at several directorates affiliated with the income and sales tax department at Jordan. The sample also includes 61 external certified auditors who were accredited by the latter department (n=93). The collected data was analyzed through the SPSS program and percentages and frequencies were calculatedIt was concluded that the application of the two tax governance mechanisms (i.e. direct and indirect tax control) can significantly reduce the levels of financial and administrative governmental corruption at the income and sales tax department. It was also concluded that external auditors - who are part of the tax governance system - play a significant role in reducing such corruption levels through enforcing indirect tax control on the income and sales tax department.Finally, the researchers recommend holding training programs to improve the efficiency of the employees working at the control departments of taxation authorities. They also recommend using advanced accounting and tax systems to raise the efficiency of the employees working at of the control department at taxation 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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".