Signs, Forms, and Models: Modeling Systems Theory and the Study of Semiosis
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.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".