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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".