The Ethics of Tax Adjudication: Initial Evidence on the Name-Letter Effect in Judicial Decision-Making
Bibliographic record
Abstract
ABSTRACT The name-letter effect is the tendency for individuals to evaluate the alphabetical initials in their name particularly favorably due to egocentric bias. We investigate whether the name-letter effect influences judicial decision-making in a tax setting. We construct datasets from decades of tax court data for two types of taxpayers (individuals and corporations) in two jurisdictions (Canada and the United States). Our total sample size is 11,370 cases. Using all possible combinations of first letter matches between taxpayers’ and judges’ names, we do not find that the likelihood of a taxpayer achieving a favorable outcome is significantly higher than if there is no name-letter match. We also match first and last names of taxpayers with judges and do not find any significant differences in outcome likelihood. Our results suggest that a psycho-linguistic phenomenon with considerable empirical support in extralegal contexts is unlikely to unconsciously bias judges. Data Availability: Data are available through the Open Science Framework at https://osf.io/3v47u/?view_only=9edcb8afa6d74783bea626126ce98bb2
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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 | 0.038 | 0.270 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".