AI-Driven Tax Analytics with Transformer-Based Text Mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The growing volume of unstructured textual data in tax ad- ministration—such as transaction descriptions, audit notes, and reconciliation narratives—has reduced the effectiveness of traditional keyword-based analytics, which fail to capture semantic meaning. This study proposes a transformer- based text-mining framework that converts narrative fields into contextual embeddings to identify tax-relevant patterns more accurately. Using a practice-as-research approach integrated with Design Science principles, the study demon- strates how low-code, freeware tools can operationalize transformer models for compliance testing. Results show improved semantic coverage and more complete detection of tax-risk indicators compared with keyword search, while remaining cost-efficient and accessible for tax professionals.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it