Bibliographic record
Abstract
Commercialization of human gametes is now legally prohibited in Canada under the Assisted Human Reproduction Act 2004, making semen donation in Canada altruistic and anonymous by law. Donors must be altruistically motivated to donate gametes without receiving monetary rewards. Globally speaking, Canada is neither the first nor the only country in the world that has legislation to support altruistic gamete donation. Other countries have advocated similar systems either through legislative changes or implementation of best practice models. This paper is a review of literature assessing the differences in donation behaviours under paid and altruistic donation models. It provides contextual information of the current semen donation situation in Canada, while drawing upon relevant literature and research data from other countries as references. The author also attempts to re-conceptualize the meanings of altruism through exploring the complex interplay between psycho-social and institutional factors in influencing donors' behaviours. Although there is a substantial amount of research studying the impacts on donor recruitment when payment is withdrawn, very few research studies are found that focus on exploring altruistic donor recruitment strategies. It is unrealistic to expect the altruistic donation culture to emerge spontaneously in Canada without any multi-level efforts to coordinate the recruitment strategies. Research programmes are greatly needed to generate empirical knowledge that can guide the development of altruistic donor recruitment models geared to the current socio-cultural environment and legislative framework in Canada. The findings will be invaluable when the legislation comes up for parliamentary review in the near future.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".