Gender and the surrogacy industry in India: An analysis of exploitation in the production of a child.
Bibliographic record
Abstract
The aim of this paper is to discuss what moral and philosophical values determine the debate on surrogacy as well as to detect the racist, gender and class oppressive discourses that prevail the surrogacy industry and exploits the surrogates labour. The study examines gestational surrogacy, which is where a couple “rent” the womb of another woman to carry their child. This is a fast growing industry, especially in India, where surrogacy, according to a report from the Confederation of Indian Industry, is estimated to generate $2.3 billion this year.The study is divided into two parts. First, it looks at reproduction issues in Western society, where most clients in the surrogacy industry come from. Second, it focuses on the surrogate and the industry in India. The first part problematizes the way our society views reproduction and what stigmas surround the notion of the nuclear family and the “need” for a biological child. The study then examines why so many childless adults now choose to proceed with surrogacy, and why they do this in India, articulating practical issues as well as the discourses of race, colonialism, gender and class that become visible. The focus in India then lies on the surrogate as well as the role of the maternity clinic. The thesis explores the dichotomy that is articulated in the surrogacy industry where the surrogate is simultaneously viewed as an object, a womb with no feelings, and as a subject, a compassionate Madonna that is impossible to objectify.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".