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Record W3015770765

Dangerous Exhibitions: Erotic Justice and Comparative Constitutional Law

2020· dissertation· en· W3015770765 on OpenAlexaboutno aff
Elena Cohen

Bibliographic record

VenueCUNY Academic Works (City University of New York) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicAfrican Sexualities and LGBTQ+ Issues
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionLawConstitutional lawEconomic JusticePolitical scienceSociologyArtArt history
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.129
GPT teacher head0.329
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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