Bibliographic record
Abstract
The Tax Court of Canada’s recent analysis, in Henco Industries Limited v. Her Majesty the Queen, raises several important questions that relate to contractual interpretation in tax disputes. Firstly, what is the proper objective of contractual interpretation in tax disputes? Is the courts’ objective of ascertaining the ‘correct tax assessment’ a proper objective of interpretation? Also, is the pursuit of a ‘just result’ in the assessment a proper objective of interpretation? Secondly, what is the proper role of the words in the contract when carrying out the task of interpretation? Is the court required, or at liberty, to look past the words and focus instead on the parties’ actions, as evidenced by extrinsic evidence of the surrounding circumstances? Thirdly, when considering extrinsic evidence, is evidence of the parties’ subjective contractual intentions admissible for the purpose of interpretation? Fourthly, under what circumstances are contractual terms ambiguous in the legal sense? Fifthly, when is extrinsic evidence of the parties’ subjective intentions admissible for the purpose of resolving latent ambiguity? The purpose of this paper is to critically analyze the court’s approach to these issues in the Henco case. While this analysis relates directly to contractual interpretation in the Canadian context, it ought to also be relevant to other common law jurisdictions that share similar principles of contractual interpretation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".