Does personality influence ratings of foreign accents?
Bibliographic record
Abstract
Abstract In both research and educational settings, native and non-native speakers are often asked to make foreign accent ratings (FARs) as a measure of second language pronunciation. However, previous research has identified several factors that influence these ratings. The current study investigates one such variable that, to date, has received little attention in the literature: personality. Thirty-six monolingual English speakers completed the Big Five Aspects Scales personality test ( DeYoung, Quilty, & Peterson, 2007 ) and provided accentedness ratings for 15 non-native English speakers (L1 Mandarin) and five native controls. Results show that two of the Big Five personality traits – conscientiousness and extraversion – were significantly correlated with the ratings listeners provided, while another trait – agreeableness – approached significance. These findings further underline the need to interpret FARs with caution, as variables unrelated to foreign accent, in this case listeners’ personality, may be associated with these ratings, as well.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".