Surrogacy: A Bio-economical Exploitation of Proletariats in Amulya Malladi’s A House for Happy Mothers
Bibliographic record
Abstract
Infertility remains a threat to global health issues thus the remedy is global with the medical advancements in technology that enable infertile couples to have babies through surrogacy. Surrogacy is a legal practice of hiring a woman’s womb for bearing a child to infertile parents where the intended parents claim full parental rights and the surrogate mothers get the monetary compensation in commercial gestational surrogacy. Surrogates are proletariats from third-world countries struggling to meet day-to-day ends, whilst intended parents are from first-world countries. The existence of poverty and poor economy avail abundant surrogates in third world countries like India attracts infertile couples to flock to these countries thus surrogacy clinics and agents practice exploitative surrogacy (baby) business. This article unveils the exploitation of poor surrogate women and their bodies, with reference to Amulya Malladi’s A House for Happy Mothers (2016). Further, this study attempts to evaluate surrogacy with the theoretical framework of Marxist Theory of Alienation or Estrangement which condemns the poor surrogates as reproductive machines (lobourers) and commercial surrogacy as commodification of wombs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".