Donor Unknown: Assessing the Section 15 Rights of Donor-Conceived Offspring
Bibliographic record
Abstract
This paper focuses on anonymous sperm donation, which has been better studied than ova donation for several reasons.Sperm donation is much more common in Canada than ova donation, although individuals are using donated ova more frequently: S Purewal & OBA van den Akker, "Systematic Review of Oocyte Donation: Investigating Attitudes, Motivations and Experiences" (2009) 15 Human Reproductive Update 499.This may be due in part to the fact that these donation processes are distinct.Unlike sperm donation, donating ova is physically intrusive and carries with it serious risks such as ovarian hyperstimulation.As a result, ova shortages are far greater than sperm shortages.This appears to be the case in the United Kingdom: Ilke Turkmendag et al, "The Removal of Donor Anonymity in the UK: The Silencing of Claims by Would-be Parents" ( 2008) 22 Int'l JL Pol'y & Fam 283 at 297. 2 Vital Statistics Act, RSO 1990, c V.4; Adoption Act, RSBC 1996, c 5. For a discussion of the movement towards greater openness, see Cindy Baldassi, "The Quest to Access Closed Adoption Files in Canada: Understanding Social Context and Legal Resistance to Change" (2005) 21 Can J Fam L 211. 3
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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".