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Record W4307835660 · doi:10.52537/humanimalia.10933

Facing Extinction

2022· article· en· W4307835660 on OpenAlexaff
Verity Burke

Bibliographic record

VenueHumanimalia · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsTrinity College
FundersNorges ForskningsrådUniversitetet i Stavanger
KeywordsArtFace (sociological concept)AestheticsVariety (cybernetics)Object (grammar)Visual artsArt historySociologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

When we consider the preservation of the animal body in natural history displays, we primarily think of techniques such as taxidermy or the mounting of a skeletal anatomy. Animal death masks are, by contrast, almost completely unstudied. Although casting has been predominantly understood as a technique for preserving the human face, non-humans have also had their faces captured by the casting of a death mask, and the resultant plaster used for a variety of purposes, from the creation of an accurate taxidermy mount, to featuring as a display object in its own right. ‘Animal Death Masks’ examines three case studies in which death masks play an integral role, all of which feature male gorillas kept in city zoos who grew to be local celebrities and were preserved for display in their regional museum, and each of whom had a cast taken of their face after death. This article argues that animal death masks materialize the distorted boundaries present in museum primate narratives: between indexical representations and artistic portraits, endangered animals and celebrity, conservation and preservation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.008
Scholarly communication0.0040.006
Open science0.0010.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0310.004

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 head0.052
GPT teacher head0.338
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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