Article published In: Diachronica
Vol. 43:2 (2026) ► pp.189–223
Testing the performance of S-curves for language change
Available under the Creative Commons Attribution (CC BY) 4.0 license.
For any use beyond this license, please contact the publisher at rights@benjamins.nl.
Open Access publication of this article was funded through a Transformative Agreement with KU Leuven.
Published online: 23 March 2026
https://doi.org/10.1075/dia.23065.nij
https://doi.org/10.1075/dia.23065.nij
Abstract
This article investigates the effectiveness of sigmoid trajectories (S-curves) for statistically modelling
language change. In diachronic linguistics, it is customary to fit a sigmoid regression line through data, on the assumption that
such a curve fits the ideal language change. A coefficient with an associated p-value significantly different from zero is taken
as evidence for the presence of a change. Here, we take the inverse perspective: given a known change, how well can the S-curve
predict the actual data? We look at 15 well-known changes in Late Modern Dutch, in a genre-balanced corpus. For each change we
built four different models: one model based on all the data and three partly blinded models, where either the beginning, middle
or end of the S-curve is omitted. We check how well the S-curve can predict or reconstruct the unknown data, i.e. the blinded
parts of the S-curve. We investigate in which cases (the type of change, the part of the data that is omitted, etc.) it is easier
or harder to reconstruct the missing data.
Keywords: S-curves, logistic regression, Dutch, drift, inflection, diachronic linguistics
Article outline
- 1.Introduction
- 2.Methods
- 2.1Residuals
- 2.2Selection of language changes
- 2.3Models and variables
- 3.Results
- 3.1Frequency of the residuals
- 3.2Linear models
- 4.Conclusion
- Notes
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