Would you hire Liam over Kirk? Name sound symbolism and hiring
Bibliographic record
Abstract
Sound symbolism is the phenomenon by which certain language sounds evoke particular associations. Previous work has demonstrated that names evoke personality associations based on the sounds they contain, with names containing sonorant consonants evoking different associations than those containing voiceless stops. Here we examined whether these associations would impact a mock hiring task. We created job ads that described an ideal candidate as being high in one of the six factors of the HEXACO framework of personality. Participants were given a pair of candidates, one whose name contained sonorants (e.g., "Molly") and one whose name contained voiceless stops (e.g., "Katie"). Whether job ads contained three personality adjectives (Experiment 1), a single adjective (Experiment 2), or a single adjective and a picture (Experiment 3) participants were more likely to choose the candidate with the sonorant name for certain personality factors. In Experiment 4 participants saw videotaped mock interviews of candidates presented with a sonorant or voiceless stop name. Names were less influential in the presence of audiovisual information than perceived name fit. These results demonstrate the impact of name sound symbolism in a more material scenario. They also help establish boundary conditions and moderators for name sound symbolism.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".