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Record W3216792745 · doi:10.29173/pandpr29503

Painting Deep Time: Encountering Landforms’ Alterity and Phusis Through Phenomenology and Oil Painting

2021· article· en· W3216792745 on OpenAlexaffvenue
Tanya Behrisch

Bibliographic record

VenuePhenomenology & Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAlterityPaintingLandformOil paintingPhenomenology (philosophy)ArtVisual artsAestheticsGeologyPhilosophyGeomorphologyEpistemology

Abstract

fetched live from OpenAlex

The practice of oil painting landforms, rocks and sea water in Jervis Inlet, British Columbia (BC) puts me in dialogue with land’s resistant alterity. By closely attuning to landforms, and by stepping back and blurring my focus at regular intervals while practicing oil painting of landforms, I experience phusis of land and of my painting. Through self-concealment and emergence, land alternates between revealing and enfolding its character, resisting my human comprehension but speaking to more-than-human elements in myself. The slow accretive process of oil painting lends itself to phenomenological research, taking days and weeks for paint to dry before new layers can be applied. This slowness produces phusis within me as an artist, as I am forced to withdraw from the painting while its layers dry and we reassume an unfamiliarity with one another as dual subjects. Through oil painting, landforms’ alterity shifts towards familiarity. Earth’s elements originate in deep time, pre-dating human experience. Cycling within me is a repository of minerals, water, and salinity originating in deep time. This draws attention to alterity within my own body. By practicing phenomenological research through painting landforms, I encounter the phenomenological paradox of deep time and come face-to-face with the originary elemental origin I share with landforms.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.322
Teacher spread0.300 · 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 designQualitative
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
Published2021
Admission routes2
Has abstractyes

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