Phoneme inventory size and the transition from monoplanar to dually patterned speech
Bibliographic record
Abstract
Atkinson (2011) shows that phoneme inventories are largest in Africa and smaller elsewhere, and suggests that this clinal distribution reflects a serial founder effect of human migrations out of Africa. Because of the way in which velaric ingressive and pulmonic egressive airstream mechanisms combine to create extra-large consonant inventories, click languages have the largest phoneme inventories of all. Critics question why phoneme inventory size, but not other properties of language, should leave a trace of the origin and dispersal of natural language. This article argues that the first modern human languages would likely have had very large phoneme inventories if we assume, following Hockett’s work (1960), that duality of patterning was the last ‘design feature’ of language to emerge. The diachronic trajectories of sign languages and writing systems illustrate that dually patterned phonologies are often preceded by a stage in which minimal units of form map directly onto semantic functions. Following Hjelmslev (1961), I label such linguistic systems, ‘monoplanar’. The article critiques language origins theories that have claimed that click consonants were sounds employed in the development of human speech because of their putatively iconic or sound symbolic properties. Focusing on the structural effects of velaric ingressives for phoneme inventory size, I argue that clicks would have been essential in elaborating large inventories, and thus large vocabularies, in monoplanar spoken languages not because of any inherently iconic properties, but because of their capacity to multiply phonemic distinctions by combining with accompaniments produced via the pulmonic egressive airstream mechanism. The contemporary distribution of phonemic clicks offers support for the hypothesis, as genetic studies increasingly point to an Eastern or Southern African origin for modern humans, while phonemic clicks have an areal but non-genetically restricted distribution in overlapping vicinities.
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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 | 0.000 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".