Acquiring Complex Focus-Marking: Finnish 4- to 5-Year-Olds Use Prosody and Word Order in Interaction
Bibliographic record
Abstract
Using a language game to elicit short sentences in various information structural conditions, we found that Finnish four- to five-year-olds already exhibit a characteristic interaction between prosody and word order in marking information structure. Providing insights into the acquisition of this complex system of interactions, the production data showed interesting parallels to adult speakers of Finnish on the one hand and to children acquiring other languages on the other hand. Analyzing a total of 571 sentences produced by 16 children, we found that children rarely adjusted input word order, but did systematically avoid marked OVS order in contrastive object focus condition. Focus condition also significantly affected four prosodic parameters, f0, duration, pauses and voice quality. Differing slightly from effects displayed in adult Finnish speech, the children produced larger $f_0$ ranges for words in contrastive focus and smaller ones for unfocused words, varied only the duration of object constituents to be longer in focus and shorter in unfocused condition, inserted more pauses before and after focused constituents and systematically modified their use of non-modal voice quality only in utterances with narrow focus. Crucially, these effects were modulated by word order. In contrast to comparable data from children acquiring Germanic languages, the present findings reflect the more central role of word order and of interactions between word order and prosody in marking information structure in Finnish. Thus, the study highlights the role of the target language in determining linguistic development.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".