Bibliographic record
Abstract
Sound symbolism links certain kinds of phonemes to perceptual properties (e.g., sonorants with roundness and voiceless stops with spikiness). Sidhu et al. (2019) applied this to names and personality traits, showing that round- and spiky-sounding names went with different personality factors. We examined name sound symbolism using the Stereotype Content Model’s warmth and competence dimensions. We also tested whether associations generalize to typically Black names, unlike prior studies that used typically culturally White names (Sidhu et al., 2019; Sidhu & Pexman, 2015). Participants (N = 66) rated 64 names on how likely they expected a person with that name to possess a given trait (related to warmth or competence). We found that round-sounding names were judged higher in warmth and competence, regardless of race or gender. Notably, typically Black names were rated higher on both dimensions. We also considered individual differences in feelings towards different groups. Our findings provide insight into sound symbolism and social cognition.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".