A Semiotic Modern Synthesis: Conducting Quantitative Studies in Zoosemiotics and Interpreting Existing Ethological Studies through a Semiotic Framework
Bibliographic record
Abstract
Abstract In this paper, I present an argument that quantitative behavioural analysis can be used in zoosemiotic studies to advance the field of biosemiotics. The premise is that signs and signals form patterns in space and time, which can be measured and analysed mathematically. Whole organism sign processing is an important component of the semiosphere, with individual organisms in their Umwelten deriving signs from, and contributing to, the semiosphere, and vice versa. Moreover, there is a wealth of data available in the traditional ethology literature which can be reinterpreted semiotically and drawn together to make a cohesive biosemiotic whole. For example, isolated signals, such as structural elements of birdsong, are attributed meaning by an interpreter, thus generating new ideas and hypotheses in both biology and semiotics. Furthermore, animal behaviour science has developed numerous test paradigms that with careful adaptation, could be suitable for use within a Peircean tripartite model, and thus give valuable insights into Umwelten of other species. In my conclusion, I suggest that by bringing together traditional ethology and biosemiotics, it is possible to use the Modern Synthesis to provide context to biosemiosis, thus pragmatic meaning to animal signals. On this basis, I propose updating the Modern Synthesis to a Semiotic Modern Synthesis, which focuses on whole-organism signals and their contexts, the latter being derived from neo-Darwinian theory and the ‘Umwelt’. Thus, there need be no dichotomy; the Modern Synthesis can successfully be integrated with biosemiotics.
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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 | 0.072 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".