Indie crowdfunded narratives of commercial surrogacy, or the contested bodies of neoliberalism: Onir’s “I Am Afia” and Arpita Kumar’s Sita
Bibliographic record
Abstract
This chapter focuses on two Indie crowdfunded narratives of gestational commercial surrogacy, “I Am Afia”, the first story in the four-part anthology film I Am (2010) by Indian filmmaker Onir (also known as Anirban Dhar), and Sita (2012), written and directed by US-based filmmaker Arpita Kumar. The protagonist of Onir’s “I Am Afia” (a web designer played by the Indian actress and director Nandita Das) is a single woman seeking IVF treatment in 2009 Kolkata; Sita is a short, twenty-minute narrative film whose protagonist rents her womb out to a Canadian woman, Kate. In different ways, these cinematic narratives offer a critique of the contested bodies of neoliberalism, speaking to the issue of surrogacy in India, a heated topic of debate in social, legal, and academic circles. Beyond categories of local and diasporic, and in line with the premise that the films of the new independent Indian cinema are glocal – global in aesthetic and local in content – these films seek to explore new subjectivities and the attached social practices. In the context of globally gendered and classed interactions and the translocal reconfiguration of family and kin structures, the new independent Indian cinema acts as a catalyst for the emergence of social change, uncovering and disrupting “traditional” social contracts. This chapter presents Onir’s and Kumar’s filmmaking as a situated artistic exercise, part of growing place-based practices which aim to be socially responsible. These arguments are sustained by an interview conducted with Onir, generous excerpts of which are given throughout the chapter.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.033 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".