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Record W2101070058 · doi:10.22329/wyaj.v31i2.4420

MARGINALIZING TRANS MEDICAL EXPENSES: LINE-DRAWING EXERCISES IN TAX

2013· article· en· W2101070058 on OpenAlexvenueaboutno aff
Samuel Singer

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsLine (geometry)BusinessPsychologyMathematics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.042
GPT teacher head0.282
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueWindsor Yearbook of Access to JusticeSame topicCorporate Taxation and AvoidanceFrench-language works237,207