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Record W4248196157 · doi:10.1386/public_00009_7

Biometric Aesthetics

2020· article· en· W4248196157 on OpenAlex

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

VenuePublic · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsYork University
Fundersnot available
KeywordsBiometricsEugenicsFace (sociological concept)SociologyPhotographyIdentification (biology)Reading (process)Galton's problemDiversity (politics)AestheticsBiometric dataEpistemologyComputer scienceVisual artsSocial scienceLawPolitical sciencePhilosophyArtArtificial intelligenceAnthropologyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract This article examines contemporary biometric science against the backdrop of its development in nineteenth century eugenic and biostatistical practices, most notably the composite photography of Francis Galton. Focusing on automated face recognition, the article argues that contemorary biometric science is inextricable from its aesthetic investments, which in turn shape the ways in which faces and bodies are differentiated in identification systems. Based on a close reading of biometric engineering texts and projects, this aesthetico-scientific approach offers new ways of conceptualizing how biometrics constitutes rather than merely reflects bodies, and encodes racist, misogynist, and other social logics into the conception and design of technologies themselves. These are not biases that can be corrected, as ostensibly progressive biometric projects like IBM’s Diversity in Faces initiative suggest, but rather are inextricable from the biometric desire to render faces and bodies as transparent and machine-readable.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.300
Teacher spread0.227 · 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