Frequency factor in the segmentation of function words in French-learning infants
Bibliographic record
Abstract
Previous work showed that 8-month-old French-learning infants segment both highly and moderately frequent function words from continuous speech [Shi and Gauthier, J. Acoust. Soc. Am. 117, 2426 (2005)]. In the present study we examine if high-frequency function words are segmented by even younger infants. Participants were 6-month-old Quebec-French-learning infants. As in the previous study, infants in the high-frequency condition were familiarized with a target functor (des or la) and were then tested with phrases containing the target versus those containing the nontarget. Thus, for infants familiarized to la, la-phrases were the target phrases and des-phrases were the nontarget phrases during Test. The reverse was the case when des was the familiarization target. The lower-frequency condition included a target functor mes versus ta (prosodically analogous to the functors in the high-frequency condition). The results show that 6-month-olds listened significantly longer to phrases containing the familiarized target than those containing the nontarget during Test, suggesting that they segmented the target. No difference was observed in the lower-frequency condition. Thus, while 8-month-olds segmented both high- and lower-frequency functors [Shi and Gauthier, J. Acoust. Soc. Am. 117, 2426 (2005)], 6-month-olds showed evidence of segmentation only for high-frequency functors. These findings suggest that frequency is a determining factor in the development of the segmentation of function words.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".