Bibliographic record
Abstract
Before looking at the problem of meaning with respect to animal communication it would be prudent to begin with an analysis of what exactly do we mean by meaning?- a dreadful tautology, but one that has preoccupied philosophers and scientists alike for thousands of years. In the everyday understanding of meaning there are three main definitions which come to mind: i) meaning as signification as in the question "What does that sign mean? " (Type1) ii) meaning as intention as in the question "What do you mean by that? " (Type2) iii) meaning as purpose as in the question "What is the meaning of life? " (Type3) These three main definitions can be joined by a score or more- the authors, Ogden and Richards in their book Meaning of Meaning (1923) for example listed sixteen categories. While there are many more, I would like to concentrate on the three main types. These are problematic enough! If we turn to the first in which we have the statement A(R)B. The standing for/signifying is a relationship (R). This relationship is dependent upon a convention. A(R)B because of C. The convention in this case is language. Even in the absence of language one requires a convention for signs. Here we can classify meaning into two types: natural meaning and conventional meaning. Natural meaning occurs outside of a system of signification like language. Often natural meaning is fixed, whereas conventional meaning is arbitrary. In Saussure's model of signification, while there is an implied referent, the process is closed, i.e. meaning is bounded by the system of language. Since there is no explicit referent, Saussure's semiology is dyadic. The model of Charles Peirce differs in almost all the dimensions. It has both natural and conventional components in its definition of meaning, it moreover has a dynamic triadic form that includes an explicit referentiality. It also includes a
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".