ArticlePoznan studies in contemporary linguistics : PSiCL2026
Evaluating the phylogenetic signal of morphosyntax.
Article in Poznan studies in contemporary linguistics : PSiCL, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational linguistic phylogenetics has so far relied heavily on cognate data. In contrast, the potential of morphosyntactic characters as a valuable source for phylogenetic analysis has been largely overlooked. We argue that morphosyntactic characters may conflate historical signal with the results of homoplasies, horizontal transfer, and universal tendencies, and must be scrutinized in terms of their propensity to change and borrowing, analogously to the curation of lexical data which produced the Swadesh lists. In this paper we make a start by evaluating a set of morphosyntactic characters based on the World Atlas of Language Structures using three methods: we (1) calculated Pearson correlation coefficients for each character against different language groupings, reflecting either shared ancestry (genera) or contact (geographical proximity); (2) counted the minimum number of mutations needed for the distribution of a character's states on a cognate-based reference tree (parsimony score), testing whether they correctly reflect language change known from historical linguistics; and (3) ran a classic hill-climbing algorithm to determine which random subsets of characters produced a phylogeny closest to a reference tree. We conclude that these are useful tools, but expect that making the definitions of the characters more theoretically informed will produce a stronger historical signal.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.