Bibliographic record
Abstract
One of the greatest conundrums in semiotics and linguistics is explaining why change occurs in communication systems. The descriptive apparatus of how change occurs has been developed in great detail since at least the nineteenth century, but a viable explanatory framework of why it occurs in the first place still seems to be clouded in vagueness. So far, only the so-called Principle of Least Effort has come forward to provide a suggestive psychobiological framework for understanding change in communication codes such as language. Extensive work in using this model has shown many fascinating things about language structure and how it evolves. However, the many findings need an integrative framework for shedding light on any generalities implicit in them. This paper argues that a new approach to the study of codes, called cybersemiotics, can be used to great advantage for assessing theoretical frameworks and notions such as the Principle of Least Effort. Amalgamating cybernetic and biosemiotic notions, this new science provides analysts with valuable insights on the raison d’être of phenomena such as linguistic change.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.019 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".