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Necrography: Death-Writing in the Colonial Museum

2021· article· en· W3135300306 on OpenAlexafffund
Dan Hicks, Priya Basil, Haidy Geismar, Marlene Kadar, Emeka Ogboh, Fernando Domínguez Rubio, Clémentine Deliss, Nicholas Mirzoeff, Bonita Bennett, Ana Lucia Araújo

Bibliographic record

VenueBritish Art Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsYork University
FundersArts and Humanities Research CouncilYork University
KeywordsColonialismArchitectureHistoryArtGenealogyArchaeology

Abstract

fetched live from OpenAlex

When it comes to the study of artworks as material culture, there are few more familiar idioms than that of the “life-history” of the object. From Arjun Appadurai’s formulation of “the social life of things” (1986) to Bruno Latour’s business-school model of “actor-networks” (1993), over the past generation a particular variety of materialist anthropology has taken root in those parts of historical studies that deal with things. “If humans have biographies, so should things”, some historians of science have proposed. In the history of art meanwhile, the reception of Alfred Gell’s influential text Art and Agency: An Anthropological Theory recast artworks as “indexes”, distributing the agency of artists, as part of the “relational texture of social life”, where biography is expanded from human into the non-human realms. As if anthropocentrism were in the top ten problems with art theory (a field that is perhaps more accurately not human enough).

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.010
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0220.002

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 head0.067
GPT teacher head0.367
Teacher spread0.300 · 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 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

Citations16
Published2021
Admission routes2
Has abstractyes

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