Bibliographic record
Abstract
Surrogacy arrangements in Canada are estimated to have increased by 400% in the last decade, in part due to the rising rates of infertility. Costs for these arrangements can be upwards of $100,000. Individuals and couples using a surrogate to expand their family have sought relief under the medical expense tax credit pursuant to section 118.2(2) or the adoption tax credit pursuant to section 118.01(2) of the Income Tax Act. The deductibility of these payments is a relatively new issue in Canadian tax law; however Canadian courts have consistently denied the deduction of surrogacy payments. The Tax Court of Canada has heard five cases on the matter and has denied the deduction in all four that have precedential value. Surrogates do not meet the definition of “patient” to qualify for the medical expense tax credit and are outside the scope of the adoption tax credit. This article canvases legal and policy arguments in favour of and against allowing surrogacy payments to be tax deductible. It proposes the creation of a new surrogacy expense tax credit, similar in design to the existing adoption tax credit.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".