A Comparison of Quantitative and Qualitative Disclosures of Tax Settlements to Assess Their Favorability
Bibliographic record
Abstract
ABSTRACT Tax footnotes, particularly disclosures of unrecognized tax benefit (UTB) accruals, contain significant information. To aid large-sample empirical research, and assess reliable measures of the favorability of tax settlements, we compare three potential proxies of footnote information: two derived from quantitative information and one derived from qualitative information. To validate the proxies, we use hand-coded disclosures about the nature of UTB settlements and an empirical model of the revaluation of prior, open tax positions at the time of settlement. We find that the textual analysis method to infer settlement favorability from qualitative text in tax footnotes performs well in both validations. Conversely, the tax rate reconciliation proxy is discriminant only in the first validation, whereas the accrual of interest and penalties proxy relates to settlement favorability only in the second validation. Overall, the qualitative proxy is most reliable and represents a new and useful measure for the empirical tax literature. Data Availability: Data are available from the sources cited in the text. JEL Classifications: C89; D81; H25; M48.
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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 | 0.017 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".