Signs, forms, and models: Thomas A. Sebeok’s enduring legacy for semiotics
Bibliographic record
Abstract
Abstract Thomas A. Sebeok has left semiotics a comprehensive theoretical apparatus for studying semiosis across species and across systems (biological and artificial). Uniting the notions of form, sign, and model into an integrative purview of meaning-making, known as modeling systems theory, Sebeok has provided a conceptual and terminological apparatus for studying all forms of meaning in terms of the fundamental “standing-for principle” that undergirds all semiotic theories. This essay revisits the Sebeokian perspective, delineating its main components in a retrospective way, highlighting its value not only to semiotics but to computer science as well. Above all else, Sebeok has made it possible concretely to establish specific interconnections between semiotics and cognate disciplines in ways that are relatively free of terminological complexities and ambiguities which have beset semiotics in the past.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".