Rudimentary phrasal parsing in preverbal French-learning infants
Bibliographic record
Abstract
The present study examines the initial parsing of noun phrases in infants. Participants were 8-month-old Quebec-French-learning infants. During Familiarization, infants heard two types of trials, one presenting a noun phrase sliced from a sentence (NP trial) and the other presenting the same word sequence (as in the NP trial) but sliced from another sentence in which these words were a part of a noun phrase and a part of a verb phrase, i.e., a non-unit trial. During Test, infants heard the original sentence containing the sliced NP and that containing the sliced non-unit. Two pairs of noun phrases and non-units were used in the experiment across two groups of infants. We predicted that if infants encoded the sliced stimuli in the NP trial as more coherent than those in the non-unit trial during Familiarization, they should listen longer to the sentences containing the NPs than those containing the same words but constituting non-units. Preliminary results show that infants listened significantly longer to the sentences containing the sliced noun phrases, even though the NP trial and non-unit trial during Familiarization involved exactly the same words. This finding suggests that salient prosodic markings enable French-learning infants to parse noun phrases from sentences.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".