Bibliographic record
Abstract
This article explores the treatment of trans medical expenses under American and Canadian tax laws. In both tax systems, medical expenses are deemed worthy of tax relief, while many cosmetic procedures are excluded. This article argues that tax administrators and the judiciary are influenced by social stigma when they employ the distinction between cosmetic and medical expenses to exclude or allow trans medical expenses. In the American context, this article focuses on the Internal Revenue Service’s reasons for deeming a trans woman’s gender dysphoria-related medical expenses to be ineligible for the medical deduction. It then turns to the taxpayer’s subsequent appeal to the U.S. Tax Court in O’Donnabhain v. Commissioner, 134 TC no. 4, and the Court’s determination that, while the taxpayer’s sex reassignment surgery and hormone therapy were eligible expenses, her breast augmentation was not deductible. The article follows by outlining the Canadian medical expense tax credit to determine how similar trans medical expenses might be treated in light of a budget amendment in 2010 prohibiting claims for most cosmetic procedures. The article concludes that in both the American and Canadian context, trans people are held to a higher standard than required under each respective tax statute, with their gender dysphoria-related medical expenses needing to be documented as “medically necessary” to avoid categorization as ineligible cosmetic expenses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".