Does the Tax System Reduce Tax Evasion in Light of the Governance Mechanisms? Evidence From Jordan
Bibliographic record
Abstract
This study aims to measure the impact of tax system elements on reducing the tax evasion, in light of the governance mechanisms in Jordan. The study sample consists of (140) tax auditors at the Jordanian Income tax and sales department, and to achieve the study objectives the researcher designed a questionnaire and distributed it on the study sample members. Study results show that elements of the tax system (tax legislations, tax administration, and Taxpayer) have a positive impact on reducing the tax evasion, in light of governance mechanisms. study recommends the need to raise the tax awareness level among members of the Taxpayer, work to reduce the continuation of making adjustments on tax laws and legislation, and give a sufficient period of time to ensure that desired economic and social impact being achieved from these adjustments, with the need to announce the official statistics of tax evasion’s figures and ratios, because the unofficial statistics on tax evasion have been tarnished by some exaggeration where work should concentrate on increasing penalties of 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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".