Visualizing Humans Contributions to Tax Complience Research: A Bibliometric Analysis
Bibliographic record
Abstract
Understanding the factors influencing taxpayer compliance with tax laws continues to be a focal point for many researchers. This study presents a bibliometric and visual analysis of tax compliance, particularly focusing on the research clusters related to tax compliance, covering studies published between 1977 and 2024 and indexed in the Dimensions database. The analysis aims to provide insights and guidance for future research in the field of tax compliance. A total of 500 studies on Tax Compliance were identified in the Dimensions database, with the majority being articles published in international journals. Australia, Austria, Canada, Italy, and the United States emerged as the most productive countries in terms of Tax Compliance publications. Additionally, a notable trend is the increasing number of multidisciplinary studies conducted by authors from various countries, particularly concerning human factors. This study recommends exploring multiple databases and refining future research through several clusters identified from these findings. Thus, researchers can gain a more comprehensive understanding of the complexity of Tax Compliance and the various clusters influencing it. The bibliometric mapping and visualization of Tax Compliance provide a foundation for advancing knowledge and promoting informed investigations in the field of human behavior regarding tax compliance
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.125 | 0.130 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".