Bibliographic record
Abstract
The study of scalar meanings or intensification has focused primarily on morphological means, yet there are many spoken languages where these concepts are expressed systematically by iconic prosody. Languages employ a combination of prosodic cues, including increased duration, raised pitch, special pitch patterns, and special voice quality, to signal scalar increases of property concepts, quantity, exhaustivity, duration, and so forth. In some languages, attitudinal meanings may also be expressed. Various labels have been used to refer to these iconic prosodic processes; below, the term prosodic intensification is used. This crosslinguistic overview looks at prosodic intensification from several angles: its phonetic realization (and orthographic representation), its meanings, its target domains, its iconic properties, and its status within each language's system (grammar or pragmatics?). It is shown that prosodic intensification is common not only in lesser-known languages but also in spoken and/or informal written registers of well-known languages and that this phenomenon is likely underreported. It is suggested that the underreporting of prosodic intensification, as well as researchers’ reluctance to treat its functions as part of grammar, is due to a persistent scholarly bias toward morphosyntactic over prosodic means.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".