The effect of tax revenues on GDP growth in Jordan
Bibliographic record
Abstract
The research aims to identify the impact of tax revenues on the growth of the gross domestic product (GDP) in Jordan during the period 2000-2018. The research reaches a set of results, which is that the greater the value of tax revenues by one unit, the greater the value of the GDP in Jordan by 7.257 units during the same period. There is also a positive effect of tax revenues on the growth and increase of the GDP in Jordan, however, there is no common integration between tax revenues and the GDP in Jordan. Moreover, there is a correction from the short term to the long term and there is an effect of the long-term correction of the relationship between tax revenues and the GDP during the study period. The study recommends the need to work to facilitate the procedures for individuals to pay taxes through modern technological means, work to develop and simplify tax services and raise the level of transparency in tax dealings with all individuals, the need to make amendments to the tax law in order to match the living conditions of individuals and achieve the highest efficiency in collecting due taxes, increasing tax exemptions that are offered to foreign investment to encourage them for investments in all economic, commercial, service and industrial fields in Jordan, working to diversify sources of income for the Jordanian economy and not to rely entirely on tax revenues as a primary source of income.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".