Bibliographic record
Abstract
This paper aims to reveal to what extent the diagrammatic iconicity (i.e., form-meaning correspondences which are created by relating similar sets of forms with similar sets of meanings [Peirce, 1955, p. 104]) of English phonaesthemes (e.g., gl- in glitter, glisten, and glow) could manifest primary iconicity (i.e., iconicity that involves a sufficient similarity between sign and referent to allow the understanding that the former stands for the latter [Sonesson, 1997]). To serve the aim, the current research conducts a test, using a multiple-choice task in which groups of native English and Korean speakers choose the meanings of phonaesthemes in sets of aurally-presented nonsense English phonaesthemic words. If primary iconicity is carried by a phonaestheme, then both native and non-native listeners should be able to report with some consistency the putative meaning of the nonsense phonaesthemic words. If, on the other hand, a form-meaning correspondence is carried by secondary iconicity (where the existence of the sign-relation, given by convention or by being explicitly pointed out, is the precondition for noticing the similarity between sign and referent [Sonesson, 1997]), then neither language group is expected to deliver high correct guessing rates. The result showed that the purported meanings of sk- and tw- were correctly guessed by the Korean-speaking participants only, and those of cl-, gl-, sw-, gr-, sn-, and sq- were correctly guessed by the English-speaking participants only. The purported meanings of sp- and tr- were correctly guessed by neither language group. These findings show that individual phonaesthemes possess varying degrees of (primary) iconicity.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".