Children’s accent-based preferences and stereotypes in media contexts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it