Does one plus one equal three in early simultaneous bilingual speech perception?
Bibliographic record
Abstract
Over half the world’s population is bilingual, yet we know little about organization of phonetic abilities in early simultaneous bilinguals. In this study we tested bilingual French–English adults who had learned and used both languages simultaneously since birth. They were presented dental and alveolar stop consonants excised from real-word productions of French /t/ and English /d/, selected to insure that VOT values overlap. The alveolar-dental place distinction is not phonemic in either French or English. However, bilingual individuals are systematically exposed to this place distinction across their two native languages. Recent findings [Sundara and Polka, J. Acoust. Soc. Am. 110, 2685 (2001)] indicate that simultaneous bilinguals clearly produce this place distinction. Perception was assessed using a categorical AXB task with tokens produced by multiple talkers in both French and English. Assimilation data were also obtained using a keyword identification task in both languages. Performance of bilinguals was compared and contrasted with those of respective monolingual groups and with native Malayalam listeners (dental-alveolar distinction is phonemic in Malayalam). The findings provide insights into perceptual organization in simultaneous bilingual adults by addressing whether an emergent contrast, not evident in either monolingual group is observed in both perception and production of bilinguals.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".