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Record W2978934231 · doi:10.1515/opphil-2019-0028

Silent Spaces: Allowing Objects to Talk

2019· article· en· W2978934231 on OpenAlexaff
Megan Sherritt

Bibliographic record

VenueOpen Philosophy · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsWestern University
Fundersnot available
KeywordsMetaphorObject (grammar)SilenceSpace (punctuation)AestheticsThe ThingOntologyEpistemologyComputer scienceArtPhilosophyLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Object-oriented ontology (OOO) is a philosophy that asks us to step outside the human-centric view of the world to recognize that objects have realities of their own. Although we cannot directly access a thing-in-itself, we can still come to know something about it through an indirect access that Graham Harman suggests is provided by aesthetics, specifically the metaphor. In the metaphor, we step into the place of the object-in-itself (that withdraws) and experience a taste of its reality. This main purpose of this article is to show that the visual arts—specifically Haim Steinbach’s art works—offer a different way to know objects. Steinbach “arranges” found objects on shelves; this emphasis on “arrangement” raises questions about the nature of the space between objects. I argue that it is this space between objects (rather than the indirect contact with objects) that grants us some access to the thing-in-itself. By relating the spaces between objects to silence, I show that it is in these spaces that objects speak. In other words, the theatricality of the metaphor Harman privileges for understanding the object only exists in a silence that emerges from the spaces between objects.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.028
Scholarly communication0.0110.017
Open science0.0020.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.049
GPT teacher head0.278
Teacher spread0.229 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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