Probability and queer expression in Sudhanshu Saria’s post-2013 <i>Loev</i> in India
Bibliographic record
Abstract
The 2013 recriminalization of homosexuality in India after the Delhi High Court had read down the anti-sodomy law in 2009. The verdict cannot be simplistically seen as a backward move. To say that it moves the country backwards or that the country is going back in time because of the said step is to fall into the trap of heteronormative prioritization. The recriminalization of homosexuality has to be seen as a violent erasure and disabling of queer narrativization of time in India. This paper presents an analysis of Sudhanshu Saria’s film, Loev (2015) to examine how queer citizen-subjects navigate the politics of invisibilization and hyper-visibilization in post-2013 India. The paper proposes the lens of Probability to examine the queer experience of how ‘now’ is occupied differently by way of structures of ambiguity. Probability can be said to be the space of slippages where heteronormative performances are used to temporarily and repeatedly hoodwink society to make possible a queer ‘now’. The paper explores the concept of Probability in relation to temporality and visibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".