Word Family Size and French‐Speaking Children's Segmentation of Existing Compounds
Bibliographic record
Abstract
The family size of the constituents of compound words, or the number of compounds sharing the constituents, affects English‐speaking children's compound segmentation. This finding is consistent with a usage‐based theory of language acquisition, whereby children learn abstract underlying linguistic structure through their experience with particular words. The family‐size effect is particularly strong for the modifier or the leftmost element. The present study tested whether the effect of family size also holds for left‐headed compounds as in French (e.g., chef de police“chief of police”) and whether the effect is due to headedness or left‐to‐right processing. Twenty‐eight French‐speaking children between 3;5 and 5;3 were asked to explain the meaning of existing compounds with constituents of varying family size. The children were more likely to mention a constituent when it came from a large family than a small family, suggesting that children's segmentation of compounds might be facilitated by analogy with existing compounds. Furthermore, as in the previous English study, children mentioned modifiers more often than heads, showing their sensitivity to the semantic roles of the constituents, rather than left‐to‐right processing.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".