Procedural Aspects of Tax Fairness: A Content Analysis of Canadian Tax Jurisprudence
Bibliographic record
Abstract
ABSTRACT We undertake a directed content analysis of Canadian tax jurisprudence to analyze procedural, interpersonal, and informational fairness, and their respective criteria, in the tax context. To facilitate our analysis, we apply Colquitt's (2001) theoretical framework of fairness. Consistent with this framework, we find 198 cases that contain procedural fairness, 34 cases that contain interpersonal fairness, and 37 cases that contain informational fairness. Furthermore, we identify seven criteria of procedural tax fairness (accuracy, bias, consistency, compatibility, correctibility, representativeness, voice), three criteria of interpersonal tax fairness (respect, propriety, timeliness), and four criteria of informational tax fairness (justification, truthfulness, full disclosure, taxpayer technical competence). Implications for taxpayers, tax authorities, and tax researchers are discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".