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Record W4252472321 · doi:10.1162/leon_a_01283

Perceptual Cells: James Turrell’s Vision Machines Between Two Paracinemas

2016· article· en· W4252472321 on OpenAlexaff
Alla Gadassik

Bibliographic record

VenueLeonardo · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsEmily Carr University of Art and Design
Fundersnot available
KeywordsPerceptionMovie theaterArchitectureProjection (relational algebra)Perceptual systemCognitive scienceComputer scienceVisual artsPsychologyAestheticsCognitive psychologyComputer visionArtificial intelligenceArtNeuroscience

Abstract

fetched live from OpenAlex

James Turrell’s perceptual cells incorporate the neurophysiological apparatus as an active participant not only in the reception of projected moving-images, but also in the very production and transmission of virtual moving-images. Combining two perceptual phenomena—the stroboscopic effect and the Ganzfeld Effect—Turrell’s perceptual cells integrate the architecture of projection with the architecture of organic vision to produce a single networked extra-sensory medium. This paper performs a phenomenological analysis of Turrell’s Light Reignfall (2011) perceptual cell, following its design, effects on the viewer, and cultural and material history. In the process, the paper situates the perceptual cell between the history of avant-garde cinema (what historians have called “paracinema”) and the history of perceptual psychology and parapsychology (what the author terms “para-cinema”). Between these two paracinemas, Turrell’s perceptual cells activate the aesthetic potential of what the author discusses as “edgeless projection.”

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)0.000
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.007
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.254
Teacher spread0.226 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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