Bibliographic record
Abstract
The study of how sounds in language are used to create words and other linguistic structures that are imitative of some sound property of their referents comes generally under the rubric of sound symbolism theory. While it has been dismissed by various approaches to language, the empirical and anecdotal support for sound symbolism is now too massive to ignore. From the database of evidence it now makes available, various hypotheses can be formulated about originating elements in word formation and, by extension, in seemingly diverse linguistic systems. One of these is the phoneme which bears suggestive meaning in itself as a primary originating element. It is thus a “modeling device” that leads, by extension, to the derivation of larger structures of meaning. Using an adapted version of modeling systems theory as developed by the Tartu School of semiotics, this paper argues that the phoneme is in fact more than a cue for distinguishing words – rather it is an elemental modeling device. This view potentially has some basic implications not only for sound symbolism theory, but also for the study of semiosis itself.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".