What’s in a Name? Sound Symbolism and Gender in First Names
Bibliographic record
Abstract
Although the arbitrariness of language has been considered one of its defining features, studies have demonstrated that certain phonemes tend to be associated with certain kinds of meaning. A well-known example is the Bouba/Kiki effect, in which nonwords like bouba are associated with round shapes while nonwords like kiki are associated with sharp shapes. These sound symbolic associations have thus far been limited to nonwords. Here we tested whether or not the Bouba/Kiki effect extends to existing lexical stimuli; in particular, real first names. We found that the roundness/sharpness of the phonemes in first names impacted whether the names were associated with round or sharp shapes in the form of character silhouettes (Experiments 1a and 1b). We also observed an association between femaleness and round shapes, and maleness and sharp shapes. We next investigated whether this association would extend to the features of language and found the proportion of round-sounding phonemes was related to name gender (Analysis of Category Norms). Finally, we investigated whether sound symbolic associations for first names would be observed for other abstract properties; in particular, personality traits (Experiment 2). We found that adjectives previously judged to be either descriptive of a figuratively 'round' or a 'sharp' personality were associated with names containing either round- or sharp-sounding phonemes, respectively. These results demonstrate that sound symbolic associations extend to existing lexical stimuli, providing a new example of non-arbitrary mappings between form and meaning.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".