Framework Proposal for the Analysis of Tax Illusion, Its Antecedents, and Consequents
Bibliographic record
Abstract
The present research aims to propose a framework to analyze the fiscal illusion, its antecedents, and consequents from the perception of Brazilian taxpayers. In terms of methodological typology, this research was framed as descriptive, using the survey as a procedure and the approach to the quantitative problem through Structural Equation Modeling techniques. After adjusting the model, the results show a significant correlation between the constructs of fiscal illusion and open data and a significant correlation between budgetary governance and fiscal illusion and between fiscal governance and fiscal illusion. Based on the analysis involving fiscal illusion and citizen participation, we conclude that there is a significant relationship between the constructs. Furthermore, the proposed integrated model indicated the possibility of improvement in the adjustment indices by considering significant correlations between the constructs concerning open data and budgetary governance, open data and fiscal governance, and budgetary governance and fiscal governance. It is inferred that the validated framework constitutes an academic implication, addressing theoretical gaps and contributing practically and with a social purpose, making it possible to improve the functioning of public administration and the provision of efficient services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".