Bibliographic record
Abstract
The beginning of the 21st century is widely seen as a time of great progress for LGBTQ people. Gay marriage, gay sex, adoption by same sex couples, and gay people serving in militaries have all been legalized in many countries in the past two decades, often through the decisions of constitutional courts. However, these constitutional protections of sexuality have been found in limited contexts and applied to a limited class of people, such that many are still vulnerable to repression by governments and majoritarian politics. In order to resist this sexual oppression, I widen the focus from gay and cisgender couples to other sexual acts and actors often left out of typical gay rights advocacy- namely BDSM, strip clubs and nudity in public. I analyze the promises and dangers of using privacy, freedom of expression, and dignity in the constitutional courts of the U.S., South Africa, India, Canada and England to protect sexuality. I show how courts reinforce the oppression of sexual acts and actors who fall outside of what Gayle Rubin calls the “charmed circle of sexuality,” often through unsubstantiated appeals to the public interest. Looking to recent Indian case law which is open to a wide range of legal and theoretical sources and which centers those outside of normatively valorized sexuality, I propose that this type of robust constitutional inquiry could be a model for other polities and provides courts with the tools to help achieve erotic justice through the law.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.080 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".