Bibliographic record
Abstract
Sound symbolism is the connection between certain features of sound and certain traits in non-linguistic items. In previous research, sound symbolic associations have been found between the height and backness of vowels, the brightness (the trait of a colour being lighter or darker), and saturation (the intensity of a colour) of colours. These same studies have also found no consistent association between colour hue (the element of colour which defines its colour classification, such as red or blue) and vowel position. We examined whether there is an association between hue and vowel position features after controlling for saturation and brightness. We selected three colour pairs matched on saturation and brightness, but differing on hue: blue/yellow, purple/orange and red/green. Participants were shown each pair, along with a pseudoword, and asked to choose the colour that best matched the word. Three pseudowords contained the front-high vowel /i/ (as in see), and three pseudowords containing the low back vowel /ɑ/ (as in saw). We examined whether participants were more likely to choose a certain colour in each pair, for either type of pseudoword. We found no relationship for purple/orange and blue/yellow colour pairs. However, there was a statistically significant relationship for the red/green colour pair, such that green was associated with high front vowel pseudowords, and red with low back vowel pseudowords. This study was conducted as a pilot study in which we attempted to artificially create associations between vowels and different hues.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".