Bibliographic record
Abstract
One areal feature of East and Southeast Asian languages is the grammaticalization of an augmentative-diminutive pair from the nominals ‘mother’ and ‘child’, respectively (Matisoff 1992). Many Sino-Tibetan languages further grammaticalize noun-class affixes from these kinship nominals, adding a parallel ‘father’ analogy in the process. Some Tibeto-Burman (TB) languages further grammaticalize the resulting kinship trio into numeral classifiers and lexical and clausal nominalizers. This paper presents evidence from the Ngwi branch of Burmic demonstrating a novel, yet parallel, polygrammaticalization process involving ‘youth’ (from TB *lak) as an analogous lexical source. Data from 30 languages inform a gradient reconstruction of two integrated, parallel clines: a nominal suffix series, YOUTH > SPROUT > SLENDER > OBLONG > GENERIC, complemented by a numeral classifier series, YOUTH(S) > AFFINAL KIN > CONSANGUINEAL KIN > NARROW > GENERIC. Both paths underlie the emergence of a collectivizing clausal nominalizer. The results support an emerging consensus: Analogy, automation and diagrammatic causation are irreducibly interdependent components of grammaticalization.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".