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Record W2563447555 · doi:10.1145/2946803.2946809

Recasting the data sublime in media architecture

2016· article· en· W2563447555 on OpenAlexaff
Claude Fortin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsMcGill University
Fundersnot available
KeywordsSublimeArchitectureArtifact (error)AestheticsThe artsMythologyVariety (cybernetics)ArtVisual artsComputer scienceHuman–computer interactionLiteratureArtificial intelligence

Abstract

fetched live from OpenAlex

In aesthetic inquiry, the sublime has remained a versatile signifier used to theorize art movements across the ages in a wide variety of artistic media. Highly influential in literature and visual arts, it has adapted to the sensibilities of changing times and objects of study. A new understanding of the concept has emerged today: the data sublime. From data visualizations to mass surveillance, the moniker data sublime has been used in the past decade to refer to the abstract manifestations of information technology in the everyday. This expression of sublimity foregrounds data as a form that is virtual, transcendent and beyond the reach of the sense apparatus. Yet data as an artifact is indelibly written and recorded by human hands. What urban representations might this paradox render possible? Are dataspaces myths or the experience of real places? This essay speculates on some of the relationships that might exist between data sublimity and placemaking in design research. It contributes to the literature by engaging in a reflection on how the data sublime could be recast and applied to media architecture. Several case studies are presented to ground this discussion and suggest potential avenues for future research.

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)0.001
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: none
Teacher disagreement score0.917
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.295
Teacher spread0.212 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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