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Record W4322628338 · doi:10.1386/eta_00119_1

The Medium Is the Environment: Digital materialism, climate crisis and digital art as pedagogy

2023· article· en· W4322628338 on OpenAlexafffund
Kevin Day

Bibliographic record

VenueInternational Journal of Education through Art · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of British Columbia
FundersCanada Council for the Arts
KeywordsMaterialismAntithesisSociologyAestheticsDigital artEnvironmental artDigital transformationCloud computingContemporary artInformation and Communications TechnologyEpistemologyMultimediaArtComputer sciencePolitical scienceWorld Wide WebPhilosophyArt historyLaw

Abstract

fetched live from OpenAlex

This article examines the environmental implications of ubiquitous information and communication technologies (ICT) manufacturing, operations and usage, using the theories of new materialism and digital materialism, and offers an interactive video installation as a case study for contemporary digital art’s pedagogical potential in response to such environmental issues. Contrary to the imageries of the cloud and the notion of immateriality as promoted by the tech industry, digital media systems are grounded in the material world and operate at the expense of the material substrate. Such a sociopolitical landscape warrants an exploration of potential counter tactics in the fields of digital art and art education. Being informed by digital materialism and hinged on the notion that encounters with contemporary art can cultivate critical and different ways of knowing, this article proposes that a focus on the material can function as an antithesis to the abstracting act of information/data and its purported immateriality.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.375
Teacher spread0.348 · 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 teacher head, not a consensus.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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