Bibliographic record
Abstract
Children spend a substantial amount of time consuming screen media—particularly television and mobile apps. Research has shown that non-native and non-standard accents tend to be underrepresented in adult-oriented and children’s media, and that representations of accents tend to be stereotypical in nature. Relatedly, research has shown that children demonstrate social preferences and ascribe traits on the basis of spoken accents. In a series of four experiments, the present research investigated children’s accent-based preferences and stereotypes in two media contexts. Children aged 5-6 and 9-10 years selected characters from a variety of exemplars that spoke in different accents to play different archetypical characters in a television program (Study 1) or to serve as teachers in an educational mobile app (Study 2). Study 1 (Experiments 1 and 2) showed that children generally preferred for television characters to speak with a native, Canadian accent (compared to high-status British and non-native accents). However, 9-10-year-old children held stereotypes about the Russian accent specifically, and preferred for negative television characters (e.g., mad scientist, villain) to speak with a Russian accent. The same pattern was not observed for Chinese or Indian accents. Study 2 (Experiments 3 and 4) revealed that children generally preferred for teaching characters in educational apps to speak with a native, Canadian accent (compared to high-status British and non-native accents), particularly when the subject matter being taught was culturally neutral (e.g., oceans, space). Children aged 9-10 preferred for teaching characters to speak with non-native accents (Russian, Chinese, and Indian) when the subject matter was culturally relevant (e.g., Russian dancing, Chinese pottery, Indian cuisine). Findings of this research contribute to our knowledge about children’s accent-based biases in social perception and accent-based assumptions about cultural expertise, and may help guide the development of more inclusive and less stereotypical media offerings for children.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".