Sorry, you make less sense to me: The effect of non-native speaker status on metaphor processing
Bibliographic record
Abstract
Preconceived assumptions about the speaker have been shown to strongly and automatically influence speech interpretation. This study contributes to previous research by investigating the impact of non-nativeness on perceived metaphor sensibility. To eliminate the effects of speech disfluency, we used exclusively written sentences but introduced their "authors" as having a strong native or non-native accent through a written vignette. The author's language proficiency was never mentioned. Metaphorical sentences featured familiar ("The pictures streamed through her head") and unfamiliar ("The textbooks snored on the desk") verbal metaphors and closely matched literal expressions from a pre-tested database. We also administered a battery of psychological tests to assess whether ratings could be predicted by individual differences. The results revealed that all sentences attributed to the non-native speaker were perceived as less sensical. Incorporating the identity of the non-native speaker also took more effort, as indicated by longer processing and evaluation times. Additionally, while a general bias against non-native speakers emerged even without oral speech, person-based factors played a significant role. Lower ratings of non-native compared to native speakers were largely driven by individuals from less linguistically diverse backgrounds and those with less cognitive reflection. Extraversion and political ideology also modulated ratings in a unique way. The study highlights the impact of preconceived notions about the speaker on sentence processing and the importance of taking interpersonal variation into account.
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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 | 0.001 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".