Proportionality and the Train of Inquiry in Tax Court Discovery: A Search for the End of the Line
Bibliographic record
Abstract
The need for reform of discovery practice in the Tax Court of Canada in larger, more complex, cases is evident. Time-intensive motions arising from disputes over the scope of discovery not only cause considerable delay and expense to the litigants; they also stretch judicial resources in a court where such resources are limited to begin with. In addition, taxpayers are disproportionately affected by discovery that too often strays into, and in some cases focuses on, areas of peripheral relevance. This article reviews current discovery practices in the Tax Court of Canada, identifies issues of concern, and recommends options for reform. The authors consider the principal causes of unfocused discovery, which include the specific procedural rules as well as the systemic dynamics of Canadian tax litigation. The authors then look to procedural reforms in other jurisdictions as offering some guidance for potential solutions, with a focus on the principle of proportionality. They challenge the presumption that strict adherence to the train-of-inquiry threshold for relevance is indispensable for a fair and effective discovery, given that other jurisdictions have generally moved away from that threshold as the sole or default standard for relevance. The authors conclude by outlining proposals for achieving a more efficient and focused discovery process to reduce costs and delays in resolution, and to ease the growing burden on the Tax Court, while preserving the fundamental objectives of discovery.
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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 | 0.046 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.056 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".