Boundary Conditions: Crossing Spatial Boundaries as a Matter of Mind
Bibliographic record
Abstract
A key step in understanding different ways of experiencing the world, consists in exploring the limits of the human mind and the languages we use to make sense of our surrounding worlds. The concept of boundary is central in this endeavor. When we think of a boundary in the broadest sense, we think of an entity (or event) demarcated from its surroundings. Whether these boundaries reflect the structure of the world or just the organizing activity of our mind is a matter of intense philosophical debate. In this paper, human spatial thinking is a starting point to further explore our interactions with and within our environment. I argue that biosemiotics offers the most suitable framework for doing so, as it integrates humans in the larger communication network flow. Yet the spatial aspect of communication has received only limited attention in the biosemiotic literature. Furthermore, basing myself on my recent crossover practice in art/science, I argue that an embodied-embedded approach is necessary to dissolve and redefine spatial categories, allowing the investigation and potential crossing of the boundaries of our perceptual worlds.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".