Painting Deep Time: Encountering Landforms’ Alterity and Phusis Through Phenomenology and Oil Painting
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.062 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".