Who can speak that language? Eleven‐month‐old infants have language‐dependent expectations regarding speaker ethnicity
Bibliographic record
Abstract
Research demonstrates that young infants attend to the indexical characteristics of speakers, including age, gender, and ethnicity, and that the relationship between language and ethnicity is intuitive among older children. However, little research has examined whether infants, within the first year, are sensitive to the co-occurrences of ethnicity and language. In this paper, we demonstrate that by 11 months of age, infants hold language-dependent expectations regarding speaker ethnicity. Specifically, 11-month-old English-learning Caucasian infants looked more to Asian versus Caucasian faces when hearing Cantonese versus English (Studies 1 and 3), but did not look more to Asian versus Caucasian faces when paired with Spanish (Study 2), making it unlikely that they held a general expectation that unfamiliar languages pair with unfamiliar faces. Moreover, infants who had regular exposure to one or more significant non-Caucasian individuals showed this pattern more strongly (Study 3). Given that infants tested were raised in a multilingual metropolitan area-which includes a Caucasian population speaking many languages, but seldom Cantonese, as well as a sizeable Asian population speaking both Cantonese and English-these results are most parsimoniously explained by infants having learned specific language-ethnicity associations based on those individuals they encountered in their environment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".