Go figural: crop circle research and the extraordinary rifts of landscape
Bibliographic record
Abstract
In recent years, cultural geographers have begun to scrutinize the relationships between the ‘ordinary’ and the ‘extraordinary’. These studies assert that the ordinary and extraordinary are not fixed and discrete, but rather, mutable and connected. The main goal of this article is to explore how landscape can combine the ordinary and the extraordinary by reflecting on my participation in the 2017 Summer Lectures Crop Circle Conference in Devizes, England, and drawing on Jean-François Lyotard’s work, Discourse, Figure (1971). My argument is that crop circles and the conference participants’ research practices landscape the ordinary and extraordinary by magnifying disruptive yet alluring rifts ( écarts) between textual acts of reading and visual acts of seeing. I illustrate how such rifts, which Lyotard aligns with ‘figural space’ ( l’espace figurai), occur on and off the conference site as follows: first, through an awkward slowness demanded by drawing crop circles in a sacred geometry workshop; second, as a result of the opaque thickness of the local countryside wherein researchers struggled to locate crop circles in fields and navigate country lanes; and third, in the operations of desire in group consciousness workshops that propelled disagreements over how to access the sacred. The article concludes by acknowledging some of the limitations of my reading of figural space, as well as some reasons why we should ‘go figural’ in cultural geography.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".