Association Between Fairness and Judicial Decision-Making
Bibliographic record
Abstract
Empirical literature on judicial decision-making has not yet considered the association between the normative value of fairness andjudges’ decisions. Using a sample of tax cases at the Tax Court of Canada (2010–2019) containing 4,420 disputes, we investigate whether a litigant is significantly more likely to obtain a favorable outcome if there is a reference to fairness in the court judgment. Compared to disputes without a mention of fairness in the court judgment, disputes with any mention of fairness have a 51% greater likelihood of a successful outcome. Notably, even when a court judgment mentions fairness not clearly in favor of the taxpayer, taxpayers are still 28% more likely to obtain a successful outcome than taxpayers without a mention of fairness. Together, these findings show that fairness is strongly associated with judicial outcomes and suggest that fairness considerations may influence judges. Furthermore, analysis of the subsample with fairness references reveals that two dimensions of fairness significantlyand positively affect the likelihood of a taxpayer winning a tax dispute (procedural fairness and interpretive fairness), and one dimension of fairness significantly and negatively affects the likelihood of a taxpayer winning a tax dispute (outcome fairness). These results show the contextual and normative importance of fairness.
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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 | 0.027 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".