Cybersemiotics: Why Information Is Not Enough (Toronto Studies in Semiotics and Communication)
Bibliographic record
Abstract
A growing field of inquiry, biosemiotics is a theory of cognition and communication that unites the living and the cultural world. What is missing from this theory, however, is the unification of the information and computational realms of the non-living natural and technical world. Cybersemiotics provides such a framework.By integrating cybernetic information theory into the unique semiotic framework of C. S. Peirce, Sren Brier attempts to find a unified conceptual frame work encompassing the complex area of information, cognition, and communication science. The integration is performed through Niklas Luhmann?s autopoietic systems theory of social communication. The link between cybernetics and semiotics is further an ethological and evolutionary theory of embodiment combined with Lakoff and Johnson?s ?philosophy in the flesh.? This demands the development of a transdisciplinary philosophy of knowledge: as common sense as well as it is cultured in the humanities and the sciences. Such an epistemologica l and ontological frame work is also developed in the book.Cybersemiotics not only builds a bridge between science and culture, but it also provides at framework encompassing them both. The Cyber-semiotic framework offers a platform for a new level of global dialogue between knowledge systems including a view of science that does not compete with religion but offers the possibility for mutual and fruitful exchange.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".