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Record W7098063541

stratification to compare their treatment of thought through geological images. In addit...

2016· article· en· W7098063541 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeotourism and Geoheritage Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsSketchRhetorical questionPaintingFunction (biology)Geologic mapOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Strata and Sediment under the Fog focuses on geological landscapes and how they are a stand-in for the mind’s landscape. This article looks at how American artist Robert Smithson describes the mind through geological landscapes in his writings and how Canadian artist Paterson Ewen diagrammatically represents the organization of rocks. The work of these two artists is considered according to the philosophies of Gilles Deleuze and Immanuel Kant—two philosophers who rely on geological and geographical models and metaphors to communicate the function of thought. Robert Smithson’s drawings of grand scale Earth-works and Paterson Ewen’s paintings of colossal earthly phenomena consider the landscape from a geological perspective. In Smithson’s art, geology is a stand-in for the topography of the mind. Ewen, for his part, sees in geological structures a matrix of organizational processes. Both artists create geological landscapes to communicate a spatial image of the mind which brings their artistic endeavours on to the plane of a particular philosophical programme. When philosophers attempt to sketch out the landscape of the mind, they resort to a rhetorical constellation associated with geography and, more precisely for the purpose at hand, geology, in order to communicate a spatial image of thought. Geology allows both artists and philosophers to produce an illustration of thought that puts the emphasis on the notion of

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.047
GPT teacher head0.242
Teacher spread0.196 · 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.

Study designObservational
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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