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Record W3197450791

Sex, Love, and Marriage: Questioning Gender and Sexuality Rights in International Law

2009· article· en· W3197450791 on OpenAlexaboutno aff
Aeyal Gross

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHuman sexualityCeremonyLawAmnestyWhite (mutation)Gender studiesPolitical scienceSociologyGirlHuman rightsPsychologyHistory
DOInot available

Abstract

fetched live from OpenAlex

The cover of Sex Rights: The Oxford Amnesty Lectures 2002 shows a picture of two men photographed from the back, with their hands holding each other's waists. They are walking towards a camera crew. Based on the way they are dressed, it seems that they have just been married. Both men are wearing white dress shirts and have similar hairstyles, with one wearing a black waistcoat over the white shirt and the other with black braces. This collection, based on the Oxford Amnesty Lectures series on gender and sexuality, thus apparently features on its cover the same-sex marriage of two men, ostensibly held in one of the few jurisdictions that have legalized such a union (perhaps the Netherlands, which was the first to do so, and was later followed by Belgium, Spain, Canada, Massachusetts (United States), and South Africa). And while we know that ‘love and marriage go together like a horse and carriage,’ what has sex got to do with this? Would it not be more appropriate for a cover of a book entitled Sex Rights to feature two persons engaged in sex or having just engaged in sex rather than a marriage ceremony? Would it not be more appropriate to depict, on a cover of a book called Sex Rights, a picture of two men in a position that suggests they have just had sex, an act for which they could be persecuted and prosecuted in various jurisdictions? So why, then, does a book on Sex Rights feature same-sex marriage on its cover?

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.302
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2009
Admission routes1
Has abstractyes

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