Discourse relativity in Russian-English bilingual preschoolers’ classification of objects by gender
Bibliographic record
Abstract
Aims and objectives/purpose/research questions: We tested whether structural relativity (i.e. the grammatical gender of words in Russian) or discourse relativity (e.g. the language spoken, cultural attitudes toward objects) would influence preschool Russian–English bilingual children’s classification of toys as boys or girls. Design/methodology/approach: We asked 20 Russian–English bilingual children to classify toys as either boys or girls, once in Russian and once in English. Some of the toys had masculine names in Russian, some feminine and some neuter. Data and analysis: We compared the children’s classifications of the toys between languages and within the items differing on gender (i.e. masculine, feminine and neuter) in Russian. Findings/conclusions: We found some weak support for structural relativity affecting children’s classifications. We found stronger effects that could be attributed to how language is used within a culture: the children classified the toys as boys more often in Russian than in English, and showed strong correlations between their two languages in how they classified toys as well as strong correlations with English monolingual adults’ classifications of the same items. Originality: This study tests the developmental course of language relativity, relying on data from bilingual children. Significance/implications: These results are consistent with a developmental account of language relativity in which some aspects of discourse relativity can emerge early, but structural relativity effects do not emerge until the middle childhood years.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".