Children’s accent-based preferences and stereotypes in media contexts
Bibliographic record
Abstract
Abstract: Children are avid consumers of screen media, including television and mobile apps. Non-native and non-standard accents are underrepresented in media, and representations are often stereotypical. The present research investigated children’s accent-based preferences and stereotypes in media contexts. Children aged 5-6 and 9-10 selected characters, from a variety of characters with different accents, to play different archetypes in a television program (Experiment 1) or to serve as teachers in an educational app (Experiment 2). Results revealed that, in Experiment 1, children generally preferred for television characters to speak with a Canadian accent (versus British, Chinese, and Indian accents), regardless of character valence. In Experiment 2, in educational apps, children aged 9-10 preferred Canadian- or British-accented teachers for culturally-neutral subjects (e.g., oceans), and Chinese- and Indian-accented teachers for culturally-relevant subjects (e.g., Chinese pottery). This research contributes to our knowledge about children’s accent-based biases, and may guide development of more inclusive media offerings. List of authors and affiliations: Kathryn Harper: Ryerson University; Lili Ma: Ryerson University
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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 | 0.002 |
| 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) | 0.005 | 0.001 |
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".