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Record W4285792842 · doi:10.1162/leon_r_02266

The Digital Image and Reality: Affect, Metaphysics and Post-Cinema

2022· article· en· W4285792842 on OpenAlexaboutno aff
Will Luers

Bibliographic record

VenueLeonardo · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsnot available
Fundersnot available
KeywordsIconMovie theaterComputer scienceDownloadCitationDigital mediaMetaphysicsWorld Wide WebArtArt historyPhilosophyTheology

Abstract

fetched live from OpenAlex

July 21 2022 The Digital Image and Reality: Affect, Metaphysics and Post-Cinema The Digital Image and Reality: Affect, Metaphysics and Post-Cinema. by Daniel Strutt. Amsterdam University Press: Amsterdam, NL, 2019. 248 pp., illus. Trade. ISBN: 978-9-46-298713-5. Will Luers Will Luers The Creative Media & Digital Culture Program, Washington State University Vancouver. Email: wluers@gmail.com. Search for other works by this author on: This Site Google Scholar Author and Article Information Will Luers The Creative Media & Digital Culture Program, Washington State University Vancouver. Email: wluers@gmail.com. Online Issn: 1530-9282 Print Issn: 0024-094X © ISAST2022ISAST Leonardo 558–559. https://doi.org/10.1162/leon_r_02266 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Will Luers; The Digital Image and Reality: Affect, Metaphysics and Post-Cinema. Leonardo 2022; doi: https://doi.org/10.1162/leon_r_02266 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. © ISAST2022ISAST Article PDF first page preview Close Modal You do not currently have access to this content.

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 score0.940
Threshold uncertainty score0.932

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.240
Teacher spread0.221 · 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
Published2022
Admission routes1
Has abstractyes

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