Substantiating an ABIL Deduction: An Analysis of the Key Elements
Bibliographic record
Abstract
For more than 25 years, Canadian taxpayers have been appealing reassessments of allowable business investment loss (ABIL) deductions. The ABIL is considered to be one of the most widely contested and frequently litigated areas in tax law, and one infused with uncertainty. Taxpayers' appeals fail almost twice as often as they succeed. In 2001, some light was shed when the Tax Court of Canada issued its decision in the Gamus case. Referring to the number of complex technical provisions that come into play when taxpayers claim an ABIL, Bowman J identified four elements that must converge if the appellant is to succeed. The purpose of this article is to present the results of the authors' review of the 240 reported court cases that comprise Canada's jurisprudence on ABILs in the context of the four essential elements. For each of these four elements, a further checklist of questions is provided to assist taxpayers and their advisers in preparing an ABIL case, along with a discussion of many of the relevant cases. An appendix to the article offers two additional tools: a table summarizing the cases by issue as well as by outcome, and a flowchart mapping the sequence of questions that, depending on the specific fact situation, may need to be answered when responding to an ABIL challenge. While there is no magic bullet or scientific formula that will guarantee taxpayers success, it is hoped that this article will be useful to taxpayers and their advisers in reducing the uncertainty in this complex area of tax litigation.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".