When the tune shapes morphology: The origins of vocatives
Bibliographic record
Abstract
Abstract Many languages use pitch to express pragmatic meaning (henceforth ‘tune’). This requires segmental carriers with rich harmonic structure and high periodic energy, making vowels the optimal carriers of the tune. Tunes can be phonetically impoverished when there is a shortage of vowels, endangering the recovery of their function. This biases sound systems towards the optimisation of tune transmission by processes such as the insertion of vowels. Vocative constructions—used to attract and maintain the addressee’s attention—are often characterised by specific tunes. Many languages additionally mark vocatives morphologically. In this article, we argue that one potential pathway for the emergence of vocative morphemes is the morphological re-analysis of tune-driven phonetic variation that helps to carry pitch patterns. Looking at a corpus of 101 languages, we compare vocatives to structural case markers in terms of their phonological make-up. We find that vocatives are often characterised by additional prosodic modulation (vowel lengthening, stress shift, tone change) and contain substantially fewer consonants, supporting our hypothesis that the acoustic properties of tunes interact with segmental features and can shape the emergence of morphological markers. This fits with the view that the efficient transmission of information is a driving force in the evolution of languages, but also highlights the importance of defining ‘information’ broadly to include pragmatic, social, and affectual components alongside propositional meaning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".