The relationship between accentedness and perceived friendliness, intelligence, and employability: A Montreal-based investigation
Bibliographic record
Abstract
The present study investigates and characterizes potential relationships between accentedness and three language attitude traits: friendliness, intelligence, and employability, in a comparison of L1 English and L1 non-English individuals. Using a direct approach method and situated in the broader English as a Lingua Franca (ELF) context, this research is intended to provide insights for the second language speaker of English regarding the perception of their accented speech. Insights into such perceptions are of high concern to the second language speaker, whose accented speech output is inherently linked with positive or negative judgments by listeners. These judgments are prevalent, subjective, and significantly impact outcomes of opportunity among second language speakers. Twelve-item questionnaires were issued to the sample population, and their responses collated and analyzed for statistical significance. The findings indicate a difference in mean ratings of the measures of friendliness, intelligence, and employability between English and non-English L1 raters, though at a significance level precluding rejection of the null hypothesis. However, significant correlations were observed between ratings of friendliness, intelligence, and employability, and between ratings of accentedness and intelligence. These findings suggest that participants perceived more highly accented speech as less intelligent. Furthermore, ratings of friendliness, intelligence, and employability were closely interrelated across participants. Additional research is suggested to evaluate these relationships, oriented around achieving a wider and more representative population sample, and further investigation of the friendliness, intelligence, and employability constructs for sub-dimensionality.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".