Research on grammatical gender and thought in early and emergent bilinguals
Bibliographic record
Abstract
Aims and objectives/purpose/research questions: This article reviews recent research on how speaking a language that marks gender grammatically might affect thinking, and on the relationship between grammatical gender knowledge of more than one language, and thinking, in both early and emergent bilinguals. Design/methodology/approach: The paper provides a comprehensive review of previous research, as well as an introduction to, and an evaluation of, the articles in this special issue. Findings/conclusions: Several themes emerge in the research on grammatical gender and thinking in bilinguals. First, knowledge of more than one language could reduce the effects of grammatical gender on thinking. Second, these effects may depend on the combination of languages being acquired. Third, researchers are starting to identify other variables that might affect when and how grammatical gender influences thinking, including proficiency and the choice of tasks. Originality: This manuscript synthesises the previously scattered research on grammatical gender and thinking in bilinguals. Significance/implications: This is the first full-length overview paper about the relationship between grammatical gender and thinking in speakers of more than one language.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".