Decriminalizing Payments Made to Surrogates: Lessons for India from Canada
Bibliographic record
Abstract
This thesis analyzes surrogacy regulation and practice in Canada and India and examines how both altruistic and commercial surrogacy have the potential to harm and exploit surrogates. I argue that money might not be the sole reason (or even the main reason) for the potential for harm and exploitation in surrogacy to become a reality. The main concern, in my view, is the regulatory approach of both countries, which fails to adequately address the myriad ethical and legal issues involved in surrogacy. I suggest policy recommendations for regulated commercial surrogacy for India, informed by the lessons learned from the Canadian and Indian surrogacy experiences. The objective of the recommendations is to safeguard and promote the interests of surrogates and prioritize their informed consent. In Chapter 1 of this thesis, I provide a brief introduction to the practice of surrogacy and reasons for its popularity in the modern world. With the help of emerging empirical scholarship from the developed world, I challenge the unsubstantiated concerns about surrogacy and lay down the foundation for my argument. In Chapter 2, I discuss how, despite Canada having a heavy-handed law on surrogacy, the law has been ineffective and has inadvertently given rise to the potential for exploitation of surrogates. In Chapter 3, I discuss the development of Indian surrogacy law and unpack the complex realities of Indian commercial surrogacy practice. I argue that India should permit regulated paid surrogacy as it will minimize the potential for harm and exploitation of surrogates. Ultimately, in Chapter 4, I provide policy recommendations for a regulated commercial surrogacy regime in India. My main recommendation is for a robust system of oversight and enforcement aimed at safeguarding surrogates from exploitation.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".