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Record W2253474432 · doi:10.1525/hsns.2015.45.1.49

Diagnosing Sex Chromatin

2014· article· en· W2253474432 on OpenAlexaboutno aff
Nathan Q. Ha

Bibliographic record

VenueHistorical Studies in the Natural Sciences · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsSex chromatinChromatinIdentity (music)Test (biology)BiologyDisorders of sex developmentPower (physics)PsychologyGeneticsPhilosophy

Abstract

fetched live from OpenAlex

In 1949, Canadian anatomist Murray Barr announced the discovery of a peculiar entity in the cell nucleus that was present in females and absent in males. The identity of this entity remained uncertain for a decade even though Barr hypothesized a relationship between it and the sex chromosomes and called it the “sex chromatin.” This hypothesis inspired the development of the chromatin into a technology that could indicate “chromosomal” or “genetic” sex, which supposedly established male and female sex difference as a binary and fundamental characteristic of humans and other animals at conception. Barr collaborated with other researchers and potential patients who applied the sex chromatin test, hoping that it could identify the “true” sex of intersexuals, homosexuals, and transsexuals. Ironically, the application of the test to intersexuals would lead to a revision of the identity of the sex chromatin itself. The history of the sex chromatin illuminates how the significance and essence of this laboratory object evolved with its use as a clinical and research tool. Researchers had hoped that the test would sort the intersex into just two categories, male and female. Instead, the sex chromatin helped to multiply categories of the intersex, distinguished them from inverts, underpinned psychosocial gender as a new dimension of sex difference, and in the process had its own identity refashioned. Today, we call it the Barr body and its story reminds us of the power and limit of biotechnologies to determine who we are.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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 designNot applicable
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

Citations6
Published2014
Admission routes1
Has abstractyes

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