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Record W2615783574 · doi:10.21785/icad2016.017

The Aesthetics Of Causality: A Descriptive Account Into Ecological Performativity

2016· article· en· W2615783574 on OpenAlexfundno aff
Teresa Marie Connors

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsnot available
FundersNewfoundland and LabradorCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of Waikato
KeywordsPerformativitySituatedPerformative utteranceSociologyImprovisationAgency (philosophy)NarrativeEpistemologyAestheticsConstruct (python library)Nexus (standard)Actor–network theoryEcologyComputer scienceSocial scienceVisual artsArt

Abstract

fetched live from OpenAlex

In this paper, I offer a perspective into a creative research practice I have come to term as Ecological Performativity. This practice has evolved from a number of non-linear audiovisual installations that are intrinsically linked to geographical and everyday phenomena. The project is situated in ecological discourse that seeks to explore conditions and methods of co-creative processes derived from an intensive data-gathering procedure and immersion within the respective environments. Through research the techniques explored include computer vision, data sonification, live convolution and improvisation as a means to engage the agency of material and thus construct non-linear audiovisual installations. To contextualize this research, I have recently reoriented my practice within recent critical, theoretical, and philosophical discourses emerging in the humanities, sciences and social sciences generally referred to as ‘the nonhuman turn’. These trends currently provide a reassessment of the assumptions that have defined our understanding of the geo-conjunctures that make up life on earth and, as such, challenge the long-standing narrative of human exceptionalism. It is out of this reorientation that the practice of Ecological Performativity has evolved.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.228
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

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