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Record W2013694747 · doi:10.1177/0306312703033001311

Dermatoglyphics and the Persistence of `Mongolism'

2003· article· en· W2013694747 on OpenAlex
Fiona A. Miller

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Studies of Science · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPleaPersistence (discontinuity)DermatoglyphicsInterpretation (philosophy)Sociology of scientific knowledgeTrisomyProduct (mathematics)SociologyEpistemologyGenealogyPsychologyHistoryLinguisticsSocial scienceLawBiologyPolitical scienceGeneticsPhilosophyEngineering

Abstract

fetched live from OpenAlex

In 1961, a prestigious group of medical researchers called on their colleagues to stop using the language of 'Mongolism' to describe people with what we now call 'Down's syndrome' (or Trisomy 21). This call responded to new knowledge about the biological basis of Down's syndrome: rather than the product of racial degeneration, as had been hypothesized in the 19th century, the condition was the result of an extra chromosome, dubbed '21'. Yet, despite this plea, the terms 'Mongol' and 'Mongolism' continued in scientific use through the 1960s. Drawing on published and archival materials, I argue that the new knowledge about chromosomes did not rupture older patterns of scientific practice or interpretation, and with them, older terminological habits. The persistence of the language of Mongolism reflects the continuity of a network of older approaches to interpreting the condition within the community of human and medical geneticists, including an enduring diagnostic and interpretive technology, dermatoglyphics. Old networks were not supplanted; they were re-aligned.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.997

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 studies0.0000.006
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 head0.027
GPT teacher head0.286
Teacher spread0.258 · 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