A QUANTITATIVE EXPLORATION OF JUDICIAL DECISION MAKING IN CANADIAN INCOME TAX CASES
Bibliographic record
Abstract
iv The dissertation explores the influences of socio-demographic characteristics of judges on their decision making in Canadian income tax cases. In analyzing historical data on judges and judicial decision making in income tax cases decided by the Supreme Court of Canada in 1920-2003 and Tax Court of Canada in 1983-2004, socio-demographic characteristics of judges are found to have influenced their decision making in income tax cases. However, the decision-influencing socio-demographic characteristics are found to steer judges to vote in different directions in the two courts. The differences are interpreted to be hints of the presence of influences other than those from socio-demographic variables on decision making in the two courts. Based on the findings on the influences of socio-demographic characteristics on the historical voting patterns, voting scenarios are constructed to show different and varied propensities to vote for taxpayers of judges of the two courts. The voting scenarios suggest that taxpayers may be more likely to win in the current Supreme Court of Canada than in the
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".