The effect of accent exposure on children’s sociolinguistic evaluation of peers.
Bibliographic record
Abstract
Language and accent strongly influence the formation of social groups. By five years of age, children already show strong social preferences for peers who speak their native language with a familiar accent (Kinzler, Shutts, DeJesus, & Spelke, 2009). However, little is known about the factors that modulate the strength and direction of children's accent-based group preferences. In three experiments, we examine the development of accent-based friendship preferences in children growing up in Toronto, one of the world's most linguistically and culturally diverse cities. We hypothesized that the speaker's type of accent and the amount of accent exposure children experienced in their everyday lives would modulate their preferences in a friend selection task. Despite literature suggesting that exposure leads to greater acceptance (Allport, 1954), we find no evidence that routine exposure to different accents leads to greater acceptance of unfamiliarly accented speakers. Children still showed strong preferences for peers who spoke with the locally dominant accent, despite growing up in a linguistically diverse community. However, children's preference for Canadian-accented in-group members was stronger when they were paired with non native (Korean-accented) speakers compared to when they were paired with regional (British-accented) speakers. We propose that children's ability to perceptually distinguish between accents may have contributed to this difference. Children showed stronger preferences for in-group members when the difference between accents was easier to perceive. Overall, our findings suggest that although the strength of accent-based social preferences can be modulated by the type of accent, these preferences still persist in the face of significant diversity in children's accent exposure. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".