Modelling the Interplay of Multiple Cues in Prosodic Focus Marking
Bibliographic record
Abstract
Focus marking is an important function of prosody in many languages. While many phonological accounts concentrate on fundamental frequency (F0), studies have established several additional cues to information structure. However, the relationship between these cues is rarely investigated. We simultaneously analyzed five prosodic cues to focus—F0 range, word duration, intensity, voice quality, the location of the F0 maximum, and the occurrence of pauses—in a set of 947 simple Subject Verb Object (SVO) sentences uttered by 17 native speakers of Finnish. Using random forest and generalized additive mixed modelling, we investigated the systematicity of prosodic focus marking, the importance of each cue as a predictor, and their functional shape. Results indicated a highly consistent differentiation between narrow focus and givenness, marked by at least F0 range, word duration, intensity, and the location of the F0 maximum, with F0 range being the most important predictor. No cue had a linear relationship with focus condition. To account for the simultaneous significance of several predictors, we argue that these findings support treating multiple prosodic cues to focus in Finnish as correlates of prosodic phrasing. Thus, we suggest that prosodic phrasing, having multiple functions, is also marked with multiple cues to enhance communicative efficiency.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".