<i>Soil</i>: Threshold Spaces of Subjectivity, Pedagogy, and Place in Landscape Art
Bibliographic record
Abstract
Abstract Through intersections of place, art, and pedagogy, normative ways of understanding landscape art and subjectivity are opened to other possibilities. With the creation of eight pieces of art, and the various activities related to them, I offer alternatives to the metaphor of wilderness that informs nationalism. With this a/r/tographical inquiry into elements of the land that serve as structural and heuristic supports, interwoven with the philosophy of Jean-Luc Nancy and Jacques Derrida, I draw on understandings of subjectivity theorized and performed from the premise that it, like learning, is an unpredictable, relational activity of emergence that exists as a threshold space. In the early spring, the Boreal is brown beneath and above me: the colour of nature’s sleep. By burying a piece of cloth, I foresee that I will initiate a drawing, making an arc of soil and dead grass. It will be a brown, gestural line cracking the future uniformity of the lawn. But that is not what happens. It all turns green and weeks later I have to move blades of grass and search carefully to find the thin scar that is the only evidence of my art. The grass covers and erases the art, laughing in silent chorus.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".