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Undergeneration and overgeneration in phonological analysis: revisiting individual and community grammars of Laurentian French high-vowel laxing

Published online by Cambridge University Press:  02 February 2026

Jeffrey Lamontagne*
Affiliation:
French and Italian, Indiana University Bloomington , USA
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Abstract

High-vowel laxing in Laurentian French is notoriously variable and complex: while high-vowel tenseness is categorically predictable in final syllables, speakers seemingly apply distinct combinations of optional processes in non-final syllables (see, e.g., Dumas 1987 and Poliquin 2006). The current study investigates laxing in non-final syllables with two core objectives: (a) to determine which grammars individual speakers have acquired, and (b) to elucidate whether subgroups within the community have distinct grammars as suggested by Poliquin or instead these subgroups are superficial categorisations (e.g., emerging from a shared community with wide distributions of possible weightings for constraints). The results reveal that a larger number of superficially distinct individual grammars emerge than were proposed in existing literature, but that these patterns fall on a spectrum centred on a shared community grammar. They also provide new evidence for the importance of prosody in conditioning phonological processes in this variety of French.

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Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press
Figure 0

Figure 1 Simulated data illustrating how constraint weight distributions’ modalities (i.e., number of peaks) and standard deviation (i.e., degree of variability) challenge conceptions of shared community grammars.

Figure 1

Table 1 The vowel inventory of Laurentian French, adapted from Côté (2012).

Figure 2

Table 2 Possible aligner outputs for /ilymin/ illumine ‘illuminate’.

Figure 3

Table 3 Distribution of individual tokens and unique types by syllable position, in addition to the number of speakers having at least one token in that syllable position for that word length. The values presented only include non-deleted tokens (i.e., those that surfaced as either tense or lax and therefore are included in the current analysis).

Figure 4

Table 4 Summary of models providing coefficient means and standard deviations (§4.1.1), normality p-values (§4.1.2), and unimodality p-values (§4.1.3).

Figure 5

Figure 2 Violin plot showing the distribution of speaker coefficients for Bayesian mixed-effect model predictors (facets). The wide horizontal line in red depicts a coefficient of zero (i.e., no effect); positive values favour lax realisations, while negative values favour tense realisations. The median coefficient is identified using the narrow blue line.

Figure 6

Figure 3 Q–Q plot of coefficients’ distribution. A relatively linear distribution in a facet is indicative of a more normal distribution of coefficients for that facet’s predictor.

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Figure 4 Correlation matrix for the coefficients of harmony predictors. Along the descending diagonal, histograms are plotted with the associated density plot overlain, giving an overview of the distribution of coefficients across speakers. A scatter plot illustrates the relationship between the two associated predictors (column and row) in the bottom left, while the corresponding cell in the top right shows Spearman’s rank correlation coefficient for that pair of predictors alongside the significance level.

Figure 8

Table 5 Clusters labelled by the exemplar speaker (selected by the clustering function) and the list of speakers in the cluster.

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Table 6 Summary of the phonological conditioning of high-vowel tenseness for each exemplar with more than one speaker (see Table 5 for lists of speakers grouped with each exemplar).

Figure 10

Figure 5 Coefficients for clusters containing more than one speaker, grouping facets by predictor.

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Figure 6 Coefficients for clusters containing more than one speaker, grouping facets by cluster.

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