The swerve: How childhood bilingualism changed from liability to benefit.
Bibliographic record
Abstract
Early research that relied on standardized assessments of intelligence reported negative effects of bilingualism for children, but a study by Peal and Lambert (1962) reported better performance by bilingual than monolingual children on verbal and nonverbal intelligence tests. This outcome led to the view that bilingualism was a positive experience. However, subsequent research abandoned intelligence tests as the assessment tool and evaluated performance on cognitive tasks, making the research after Peal and Lambert qualitatively different from that before their landmark study, creating a disconnect between the new and earlier research. These newer cognitive studies showed both positive effects of bilingualism and no differences between language groups. But why were Peal and Lambert's results so different from previous studies that were also based on intelligence tests? The present study analyzed data from verbal and nonverbal intelligence tests that were collected from 6,077 participants across 79 studies in which intelligence tests were administered as background measures to various cognitive tasks. By including adults, the study extends the results across the life span. On standardized verbal tests, monolinguals outperformed bilinguals, but on nonverbal measures of intelligence, there were no differences between language groups. These results, which are different from those reported by Peal and Lambert, are used to reinterpret their findings in terms of the sociolinguistic, political, and cultural context in which the Peal and Lambert study was conducted and the relevance of those factors for all developmental research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 | 0.018 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".