Bibliographic record
Abstract
Over the past two decades, a growing number of donor conceived people have spoken out about the impact of donor anonymity on their health and wellbeing. A significant number of legislatures have responded to these concerns by introducing laws that prospectively (and in one case, retrospectively) abolish donor anonymity. This article considers the increasing pressure on Canadian provinces to end anonymity and introduce registers which enable donor conceived people to access their donor’s identifying information. While the article does not endorse the genetic essentialism that is often a feature of advocacy in the field, it does argue that there are no longer grounds upon which Canada can justify the practice of prospective anonymity. Substantial evidence suggests that the wellbeing of future generations of donor-conceived people is best met by providing them with the option of accessing their donor’s identity. What has received less attention in the literature is what type of open disclosure model should be adopted. Decisions need to be made about issues such as whether legislation should be prospective or also retrospective in its operation, how many families a donor should be permitted to donate to, if and how donor offspring are to be notified of the nature of their conception, and how the expectations of participants are to be managed.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".