Bibliographic record
Abstract
Could a theory of aesthetic sense-making help move domain-general theories of evolution beyond mere Darwinian assumptions? To show how this could work, I apply C. S. Peirce’s approach to analogy as active-inference and his triadic theory of evolution to an original language development case study involving novel paradigm formation over a six-week period by a child aged 1;07–1;09 in the domain of free play focused on a set of blocks. In referring to the blocks, the child produces creative lexical blends involving abstractions drawn from shape, colour, and food-based iconicities. In the process, evidence for three interacting (irreducible but interdependent) modes of language evolution emerge that are arguably domain-general: analogy, automation, and diagrammatization (previously defined in Pelkey 2013, 2015, 2019). The bridging connections in question require a process-oriented semiotic perspective grounded in the experiential, or tonal, relations of iconicity. The argument helps clarify the aesthetic nature of analogy by highlighting distinctions between analogic agency, automated processing, and diagrammatic process in ways that are germane for evolutionary theory in general, to better clarify the relationship between aesthetics as semiotic fitting (Kull 2022) and diagrammatization as semiotic evolution.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".