Unblocking the implementation of altruistic surrogacy in Portugal: inclusive legal reform proposals through a comparative analysis
Bibliographic record
Abstract
Abstract This article examines the Portuguese surrogacy regime, which has undergone significant modifications over eight years but remains unimplemented. It traces the evolution of surrogacy law in Portugal from its legalization in 2016 up to 2024, emphasizing the impact of two unconstitutionality judgments and presidential vetoes. It then identifies practical and legal obstacles preventing policy implementation: a lack of resource allocation to the competent body and uncertain legal parenthood once the surrogate revokes consent. To propose solutions to some problems derived from its legal regime, a comparative methodology is employed, exploring altruistic surrogacy in the UK, South Africa, and Ontario (Canada). These systems, operational for years and sharing characteristics with the Portuguese model, provide insights into its strengths and weaknesses. This cross-national study presents reform proposals to address some legal challenges of the current regime, which could facilitate inclusivity and the successful implementation of surrogacy in Portugal. Recommendations include adopting a broader definition of infertility and establishing provisions for triple legal parenthood in cases where a surrogate revokes consent. The paper argues that substantial time and effort have been invested in developing this regime. Therefore, once the most critical issues are addressed through further regulation, policy implementation should be prioritized. The paper concludes that additional improvements in surrogacy law can only occur if the practice is operational in Portugal.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".