The empirical analysis of fiscal illusion
Bibliographic record
Abstract
This research aims to find the tendency fiscal illusion's existence in regencies/cities of South Sumatera Province during the period 2012 -2018. In addition to detecting fiscal illusions, the study aims to determine the factors that explain the estimated value of detected fiscal illusions. To detect fiscal illusions, the study uses 3 approaches divided into 3 models, namely revenue enhancement, expenditure manipulations, and debt utilization. The sample of this research is 15 regencies/cities in South Sumatra Province. The analytical method used is panel data regression. The results of this study show that there was a fiscal illusion detected in regencies/cities of South Sumatera Province in the 2012-2018 period through the expenditure manipulation approach. Besides that, the test results also show that all variables in the expenditure manipulation approach affect and are able to explain the detected fiscal illusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".