Signs, Forms, and Models: Modeling Systems Theory and the Study of Semiosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The approach to studying signs and sign systems, known as modeling systems theory, derives from the work of the Moscow-Tartu School of semiotics. After being largely excluded from mainstream semiotic theory and practice, it is now becoming more and more a major trend in semiotics. The theory envisions a sign structure (or form) as a model of some referent and that the models we make of the world become signs that elicit interpretation of that world. The theory has been applied to the study of biological systems, mathematical cognition, and the origins and development of human cultures. This paper presents an overview of modeling systems theory; differentiation among "forms", "signs", and "models" as separate, yet interrelated, dimensions of semiosis. It describes the features of these dimensions, integrating them into an overall theory of semiosis. The main aim is to synthesize several of the suggestions that the present author has previously put forward in this regard and which reflect a growing trend in semiotics to revisit basic sign theory in terms of the concept of modeling systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it