Bibliographic record
Abstract
Tax is considered an undeniable reality in human societies.This fact exists in rich countries with natural resources as well as the ones lacking such wealth.Today, one cannot study an economy in the world that does not recognize tax as a contributing factor.In Iran, according to the fourth development plan, credit costs should be supplied from non-oil sources, and according to statistics, achieving this goal seems difficult.Presence of effective tax policy is one of the important factors in achieving tax goals.However, formulation of these policies is a multidimensional and difficult task and requires benefitting from collective wisdom of main beneficiaries of tax in various sectors of economic activities.The purpose of this study is to develop policies to increase tax revenues of the government, their evaluation, and the quality of effects of policies on economy.For this purpose, we tried to identify the policies and study their effects in form of Delphi process and multiple targets using tax-economy experts' views.Moreover, because of differences in perceptions of the words low, medium and high, we studied the responses of the experts as fuzzy numbers.Delphi process was conducted in the study with multiple goals, ended after three stages, experts reached a consensus over their views, and identified appropriate policies to increase tax revenues and improve economy.Experts examined each of the proposed policies separately, and their opinions about the intensity of effectiveness were evaluated using Delphi method, so that the experts reached consensus at the end.Using Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Delphi, we ranked effective tax policies.The results of this study have explained priority of tax policies to achieve the purpose of economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.951 | 0.935 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".