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Record W4409505003 · doi:10.4324/9781003537670-7

Many Conceptualities

2025· book-chapter· en· W4409505003 on OpenAlexaboutno aff
Robert Bailey

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This chapter considers connections between conceptual art, art history, and Indigeneity. Through analysis of art-historical work by Ian Burn and Lucy R. Lippard motivated by encounters with Indigenous art (Aboriginal Australian and Native American, respectively), it argues that the ways in which these two unconventional art historians received and responded to that art reflects their earlier participation in the conceptual art movement. It also takes into consideration how Indigenous artists such as Gerald Clarke (Cahuilla) address conceptuality as a concern in their work. Readings of Burn’s work (much of it collaborative with Ann Stephen) on Arrernte artist Albert Namatjira show how Burn moved past prevailing understanding of the artist’s watercolors as imitative of white art to identify a conceptually savvy practice staged effectively between cultures. Tracing the trajectory of Lippard’s work, especially after she relocated to Galisteo, New Mexico, and dialoged with Native American artists including Jaune Quick-to-See Smith (Salish-Kootenai, Métis-Cree, and Shoshone) and Ramona Sakiestewa (Hopi), shows how she reconceived her role as an art historian to focus on questions involving identity, locality, environment, and place. Overall, this chapter recognizes the complex and fraught space in which Indigenous and non-Indigenous artists and art historians shape the concept of art.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0100.043
Scholarly communication0.0130.018
Open science0.0020.008
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0160.002

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 head0.072
GPT teacher head0.219
Teacher spread0.147 · 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.

Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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