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Record W1760711892 · doi:10.1080/10304312.2015.1073690

The ‘Information Landscape’ of Iain Baxter&

2015· article· en· W1760711892 on OpenAlexaff
Adam Lauder

Bibliographic record

VenueContinuum · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicArt, Technology, and Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMainstreamEmbodied cognitionEnvironmentalismSociologyMedia theoryEpistemologyAestheticsArt historyPhilosophyArtMedia studiesLawPolitical sciencePoliticsTheology

Abstract

fetched live from OpenAlex

This article examines the conceptual artist Iain Baxter&'s transformation of landscape conventions under the influence of media theory and western popularizations of Zen philosophy. The artist's McLuhan-inspired representations of ‘information’ provided a foundation for his prescient ecological critique of first-generation American Information Theory, beginning in the mid-1960s. This critical engagement with mainstream constructions of information and the environment is explored in relationship to the still relatively unknown works of information art produced by the corporate ‘umbrella’ N.E. Thing Co. Ltd., which the artist co-administered with Ingrid Baxter from 1969 to 1978. The embodied and environmental orientation of NETCO's art of ‘Sensitivity Information’ challenged the analytic claims of New York- and UK-based conceptual peers, including Joseph Kossuth and the members of Art & Language. The present discussion traces the legacy of those early experiments in more recent bodies of work inflected by the artist's deepening commitment to environmentalism.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.016
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.024
Scholarly communication0.0160.016
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.026
GPT teacher head0.218
Teacher spread0.192 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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