The Peircean order of signification and its encoding system in Chinese landscape painting
Bibliographic record
Abstract
Abstract Applying Peirce’s semiotics to the study of art history, this essay explores the order of signification in the Peircean theory and the visual order in Chinese landscape painting. Since the purpose of Chinese landscape painting is not simply to represent the beauty of scenery but to encode and manifest the philosophy of Tao, then, the author argues that the establishment of the encoding mechanism in Chinese landscape painting signifies the origination, development, and establishment of this genre in Chinese art history. In this essay, the Peircean order of signification is described as a T-shaped structure, consisting of a horizontal dimension of signs (icon, index, and symbol) while and a vertical dimension of the signification process (representamen, interpretant, and object). Correspondingly, the visual order in Chinese landscape painting is also described as a T-shaped structure as well: the horizontal dimension at the formal level consists of three signs (mountain path, flowing water, and floating air, the three constitute a compound sign), while the vertical dimension at the ideological level consists of three concepts (the way in nature, the metaphysical Way of nature, and the Tao). The significance of this order is found in re-interpreting the formation of landscape painting in Chinese art history.
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 | 0.001 | 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 | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".