“Tony Danza Is My Sperm Donor?”: Queer Kinship and the Impact of Canadian Regulations around Sperm Donation
Bibliographic record
Abstract
Based upon an empirical research study of lesbian, gay, bisexual, trans, two-spirit, and queer (LGBTQ) people accessing reproductive technology, this article aims to lay out some concerns around the use of third-party donor sperm in Canada. It tracks the new forms of lateral kinships being created and the ways in which they may exert a differentiated impact on LGBTQ communities. The article overviews relevant federal regulations and legislation and uses this grounding to investigate the case study of a lesbian couple in Toronto and their experience with anonymous donor sperm imported from the United States. Their story helps to highlight the many lacuna that exist in the present regulatory regime and demonstrates how LGBTQ people are placed disproportionately at the fore of these pressing legal gaps. Ultimately, while the article argues that the effects of poorly crafted legislation around semen donation may be pronounced in LGBTQ communities, these effects may be experienced by all users of anonymous third-party sperm. By centring the queer family at the heart of the analysis, however, this article calls for a fresh look at how reproductive projects through assisted technology are being pursued under the present Canadian legal regime.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.025 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".