1.1 Gaelic dialectology: Previous work

Studies of Scottish Gaelic dialectology tend to concern either single varieties or isolated features across a range of dialects. No macro-study of Scottish Gaelic dialectology has appeared to date, at any linguistic level. The LSS(G) was the only systematic effort to capture the full extent of the language’s geographic variation, and only a fraction of it has been analysed. Barring pioneering papers by Jackson (1967; 1968), most of the inductive work has only occurred since the appearance of the Survey of the Gaelic Dialects of Scotland, or SGDS (Ó Dochartaigh 1994–1997), the five-volume collection of contextualising essays and edited phonetic transcriptions from the Survey (see Section 2.1). While certain Gaelic dialects are reasonably well documented, our understanding of the language’s macro-variation remains inchoate.

The Scottish Gaelic dialects are at one end of the wider Goidelic continuum, which includes Irish and Manx Gaelic. Only O’Rahilly (1936) has attempted to describe the dialectal divisions of the entire Gaelic-speaking area, but some useful regional studies can be found, such as Borgstrøm’s volumes on Skye and Ross-shire (1941) and the Outer Hebrides (1940), and Ó Dochartaigh’s study of Ulster Irish (1987). Robertson’s (1897; 1898; 1907) early studies of Scottish Gaelic also deserve mention. Most of these studies note morphosyntax in passing, if at all, but some provide observations useful for the present context.

On a phonological basis, Robertson (1907) concludes that only two main dialects, a Northern and Southern one, can be identified conclusively: “It would perhaps be as easy to distinguish thirteen dialects as three”.³ Jackson (1968) agrees that the Gaelic dialects form two main divisions, but views their orientation differently. For Jackson, the key division is not north and south, but north-west and south-east, encapsulated in zones which he terms the “centre” (north-west) and the “periphery” (south-east), as shown in Figure 1. Jackson identifies a single central dialect – “on the whole […] innovating […] and fairly homogeneous” (Jackson 1968: 67) – while he regards the peripheral dialects as fragmented and more conservative.

Figure 1

Kenneth Jackson’s division of Gaelic dialects. The dotted line shows the approximate eastern boundary of the Gaelic-speaking area in the 1870s (Withers 1984: 92 sqq.).

Watson (2010) provides an overview of Scottish Gaelic macro-divisions and seemingly contradicts Jackson, by describing the western region as more conservative than the east. Watson is one of the few authors to consider morphosyntactic variation, on which he bases his statement. Several studies have found the peripheral region to be phonologically conservative, but morphosyntactially progressive (e.g. Dorian 1978a; Ó Murchú 1989). Our findings (see Section 3) support this, but, surprisingly, we find high levels of morphological innovation even in certain “central” sub-dialects, such as Assynt and Lewis. This challenges the claim that the entire Hebridean area is morphosyntactically homogeneous (e.g. Borgstrøm 1940: 8; Watson 2010: 108). While we could note additional work on the morphosyntax of single dialects and dialectal sub-regions (cf. Adger & Ramchand 2006; Cole 2015; Dorian 1973; 1978a; b), we are not aware of further scholarship on the macro-variation of Goidelic morphosyntax.

1.2 Morphosyntactic dialectometry: The general context

Morphosyntax has been considered since the beginnings of dialectometry (e.g. Séguy 1971; Goebl 1982), but dedicated studies are relatively scarce (Wieling & Nerbonne 2015: 256). In a programmatic review, Glaser (2013) concludes that we know far less about the dialectal variation of morphosyntax than other linguistic levels. The first dialectometric study of a language’s morphology was Heeringa et al. (2009), which examined Flemish and Netherlandic data from the Morphological Atlas of Dutch Dialects (De Schutter et al. 2005; Goeman et al. 2009). Spruit (2008) is the first large-scale investigation of syntax known to us, using data from the Syntactic Atlas of the Dutch Dialects (SAND) (van der Ham et al. 2005). He showed that Dutch dialects could be categorised using pairwise measures of syntactic distance and that the results resembled earlier, subjectively-derived atlas maps. He also examined the extent of correlation between different linguistic levels for Dutch dialects, finding that syntax was strongly correlated with phonology, but only weakly with lexis. More recently, Scherrer & Stoeckle (2016) examined the overlap between lexis, phonology, morphology and syntax for Swiss German dialectal variation and measured their individual correlations to geographical distance. In contrast to Spruit (2008), they found that the syntactic data were outliers and less geographically coherent. Morphology was found to have the most geographic coherence (cf. Heeringa & Hinskens 2014).

Szmrecsanyi analysed the geographical variation of English morphosyntax in various publications, using data from the Freiburg Corpus of English Dialects (e.g. Szmrecsanyi 2011; 2013; Wolk & Szmrecsanyi 2016). An important finding in Szmrecsanyi (2013: 151) was that, while only 25% of the 57 features examined patterned diatopically on their own, the other 75% contributed to the aggregative analysis: “even geographically seemingly irrelevant features contribute to and are indispensable for an accurate description of the big picture”. Kortmann (2013) used one of the largest datasets of English morphosyntax (World Atlas of Varieties of English) to investigate the importance of geographical distance as a predictor of morphosyntactic variation across the sample. Finally, in a study similar to our own, Aurrekoetxea (2016) examined the variation of Basque dialects in terms of nominal morphology.

Perhaps surprisingly, the Goidelic languages have a long, albeit sparse, history of studies that can be described as dialectometric. A pioneer in this respect was Robert Elsie, who conducted dialectometric studies of the lexicon of both the Brythonic (Elsie 1983) and Goidelic (Elsie 1986) languages. His work, however, has not been particularly influential among Celticists (Ó Muircheartaigh 2014). On the other hand, Kessler (1995), who worked on Irish Gaelic, is notable for pioneering edit distance in dialectometry; his study has been influential across the discipline at large.

3.1 Aggregate measure of conservatism

Jackson’s choice of features for the morphological sections of the LSS(G) was wholly intentional; he selected those belonging to a perceived grammatical ideal known sometimes as “Biblical Gaelic”. Most of the norms of this variety stem from Classical Gaelic (or Early Modern Irish), a conservative grapholect utilised from the end of the 12th century (Ó Cuív 1983: 3; McManus 1994) until the collapse of the Gaelic learned orders in the 17th (Ireland) and 18th (Scotland) centuries. Although Classical Gaelic was never institutionally imposed, its characteristics have informed literary standards into modern times.

We derived an aggregate measure of how close a dialectal point is to a conservative maximum. Almost all of our features were coded so as to make 1 correspond to a return that agreed with the conservative norm and 0 to an innovative form. Therefore, an aggregate measure of conservatism can be derived by simply summing the responses for all features.⁸ Missing responses were treated as zero, as they also likely indicated a deviation from the conservative ideal.

As noted in Section 2.4, there were a total of 55 features; therefore, 55 is the maximum possible conservatism score. The descriptive statistics for the score are shown in Table 1. The maximum score found in the data is 50, at point 28 (Castlebay 1, Isle of Barra). Half of all observations lie between the values of 15 and 27. The distribution of the scores on a histogram with a bin width of 5 is shown in Figure 2.

Figure 2

Distribution of the aggregate conservatism score.

It is notable that no point returned the maximum possible score of 55, corresponding to full agreement with the conservative ideal. The distribution of the scores in space is shown in Figure 3. The figure shows a pronounced east to west cline, from grammatically progressive in the east, to conservative in the west. The general pattern is interrupted occasionally by outliers, but we can explain at least some of these cases in terms of idiolectal variation. For instance, the contributor from Canna, one of the most conservative points, had a well-known seanchaidh (expert in oral tradition) for a father. Additionally, this individual’s mother and husband were from Barra, the most morphologically conservative region in the LSS(G) materials. The informant from Dornie, in Kintail, was himself described as a seanchaidh with unusually archaic forms. Similarly, while the results for the Isle of Lewis diverge from the rest of the Outer Hebridean dialects and show similarities to the adjacent mainland in Wester Ross,⁹ point 3 (Bragar) stands out as particularly conservative. Looking at Jackson’s notes, we find that the contributor for Bragar was very literate and “perhaps too sophisticated to be an ideal informant”.

Figure 3

Distribution of the conservatism score.

3.2 Cluster analysis using edit distance

To identify whether the morphological variation observed in the data is congruent with the present state of knowledge on Gaelic dialect divisions, we subjected the data to a clustering analysis. In this section we report the results of this analysis when conducted on the raw questionnaire returns.

Since Gaelic orthography marks many of the relevant phonological and morphological distinctions, orthographic representations are suitable for dialectometrical methods using edit distance metrics such as Levenshtein distance (e.g. Nerbonne & Heeringa 2010). We converted the raw phonetic transcriptions into Gaelic spelling, normalising the otherwise often very narrow phonetic transcription of the questionnaire returns. We used a slightly modified version of Gaelic orthography, omitting some graphical devices that would artificially inflate the edit distances. In standard spelling, t-sandhi – the replacement of an initial [s] by [t] in certain grammatical contexts – is expressed by <t-s>, as in sùil ‘eye’ [suːl] but (leis an) t-sùil ‘with the eye’ [tʰuːl]. The string distance between <s> and <t-s> is 2 (two insertions), although in reality we are dealing with a single edit (substitution of [t] for [s]). To avoid this, we retranscribed <t-s> as <t>.

As noted in Section 2.4, there were no returns for some of the prompts. Treating the missing data as empty strings produces artificially high distances: the edit distance between an empty string and a non-empty one equals the length of the non-empty string, producing on our data distances of 10 and more. To avoid these artefacts, we imputed the missing data by replacing blank returns with the mode (the most frequent response) for that prompt. The effect of this is to make dialects with missing points more similar to the “average” dialect, and reduce the likelihood of spurious outliers that might influence the clustering algorithm.¹⁰

We used the R package stringdist (van der Loo 2014) to calculate Levenshtein distances between the enquiry points for each of the prompts, and then summed the pairwise similarity scores for each prompt to arrive at a total similarity matrix. This was then subjected to a nested agglomerative clustering procedure using the Ward method, with the function agnes in the R package cluster (Maechler et al. 2015). We computed the average silhouette width for a number of partitions (between 2 and 10) to choose the best number of clusters. Silhouette width is a rough measure of how well each observation conforms with the other observations in the same cluster; in other words, partitions with a high average silhouette width mostly consist of internally coherent clusters, whilst low average silhouette widths signal that the partition does not do a particularly good job of capturing dissimilarities in the data. As a rule of thumb, average silhouette widths of <.2 can be interpreted as indicating lack of informativeness in a partition. On our data, the biggest number of clusters with an average silhouette width of >.2 was three. This clustering is shown in Figure 4.¹¹

Figure 4

Clustering of dialects based on edit distance.

The pattern that emerges from this exercise again shows a pronounced cline from the east to the west. The “eastern” cluster is concentrated on the mainland, covering basically all the points not on the western seaboard, in addition to the western and northern coasts of Sutherland, most of the western coast of Ross-shire and part of Lewis, as well as all of mainland Argyll south of Loch Linnhe and the islands of Arran and Islay. The “western” cluster includes most of the Inner Hebrides, Skye, and northern Argyll, and a few points in Lewis and Harris. Finally, points belonging to the “Hebridean” cluster are concentrated in the Outer Hebrides south of Harris.

At a very simplistic level, the existence of such a cline may indicate a relationship with the strength of the language in the respective communities. Indeed, the “Hebridean” cluster includes large parts of today’s “Gaelic heartland”, in areas such as Uist and Barra. On the other hand, points as far apart as Easter Ross, Central Perthshire, Kintyre, and Arran end up together in the same cluster, and they are all places where the regional variety of Gaelic is now extinct or moribund.

Nevertheless, this generalisation does not always hold up. It is true that the “eastern” cluster contains most of the points with the lowest percentages of Gaelic speakers. Yet, several points belonging to it did have high or very high percentages of Gaelic speakers in 1891, such as Lewis, Applecross and north-west Sutherland (the area known as Strathnaver, or Mackay Country). Still, most of the points in the “Hebridean” cluster are near the top of the ranking of percentage of Gaelic speakers, whilst the “eastern” cluster consists mostly of points where about half the population was recorded as Gaelic speakers in 1891. Thus, we can say that the overall cline in this case is consistent with having some kind of relationship to language endangerment. Yet, in certain cases the correlation can break down, particularly in the north-west Gaelic-speaking area.

3.3 Cluster analysis of morphological features

Recall that the morphological data from the Survey materials were coded in a binary fashion, for the presence or absence of a particular feature. Since this coding treats the outcome variable as categorical rather than continuous, many clustering methods popular in the dialectometric literature (such as k-means clustering or agglomerative nesting) are not appropriate for our data. Instead, we conducted a multiple correspondence analysis (MCA). Multiple correspondence analysis (e. g. Husson, Lê & Pagès 2011) is a method for reducing the dimensionality of the data. It is similar in intent to methods such as principal components analysis or factor analysis, in that it produces a set of dimensions (smaller than the original number of variables) and loadings for each data point in every dimension, so that the data points with a similar profile have similar loadings. Once the discrete data are represented as a set of continuous loading values, hierarchical clustering procedures can be applied; this method is known as hierarchical clustering on principal components (HCPC).

We conducted this analysis using the functions mca (for the multiple correspondence analysis) and HCPC (for the clustering) from the R package FactoMineR (Lê, Josse & Husson 2008). As with our conservatism score, we coded missing values (i.e. absence of a return) as negative returns, since either rejection of a conservative form or a missing return indicate that the expected conservative form is unlikely to be accepted by the informant.

The outputs of MCA are suitable for agglomerative clustering of the kind described in Section 3.2 above. In order to identify the best number of clusters in this case we used inertia gain, a measure widely accepted as appropriate for HCPC outcomes. Inertia is a measure of the heterogeneity of the data, both within and across the different clusters: if adding a partition leads to a significant drop in inertia, then adding such a partition is commonly considered meaningful, whereas partitions that only contribute modest drops in inertia are less likely to represent meaningful structure in the data. In our data, the best supported partition is into 4 clusters. Its spatial pattern is shown in Figure 5.

Figure 5

HCPC result with 4 clusters.

Once again, we observe an east-west cline. It is, if anything, even more pronounced than in the case of clustering based on edit distance. Thus, what we call the “eastern” cluster is concentrated along the eastern periphery in Perthshire, Nairnshire and the Ross-shire eastern seaboard. The next cluster, which we call “central”, is located further to the west on the mainland. It only occasionally touches the western seaboard, except in Argyll, but, remarkably, it includes both the southwestern-most points of the survey in Islay and far north-east of the Gaelic-speaking area in North Sutherland. A further one, the “western” cluster, covers most of the western seaboard, Mull, Skye, and (at least the northern part of) Lewis. Finally, the “Hebridean” cluster covers the area we identified in previous sections as relatively conservative, including most of the Hebridean chain from Harris southwards.

So far, the results of our study of morphology do not support the division of Gaelic dialects into a “centre”, covering the Hebrides and large parts of the western mainland, and a “periphery”, as offered by Jackson (1968), primarily on the basis of phonological criteria. What our results are reminiscent of is the dynamics of language endangerment, with a demographic “heartland” in areas such as the Outer Hebrides, Skye, and the western Highlands, and a much more patchy distribution of Gaelic speakers in the eastern and southern periphery, in places such as south Argyll, Perthshire, and the far north-east. The pronounced overall cline strongly suggests an explanation with roots in language attrition under conditions of progressive, subtractive bilingualism and the gradual weakening of Gaelic-speaking communities under the encroachment of English, with concomitant loss of morphological categories.

Cluster analysis does not really allow us to examine the potential role of demography and language endangerment in the development of morphosyntactic patterns. Pioneering work by scholars such as Dorian (1973; 1978a) has demonstrated the existence of this connection at a “local” level, i.e. within a single variety; however, the clustering analysis does not allow us to verify whether such a connection is also present across the board. To remedy this, we conducted a regression analysis to probe the connection.

3.4 Regression analysis: Conservatism and demography

The results of the cluster analysis described in the previous two sections allow us to discern an overall qualitative pattern of similarity between varieties. However, the cluster analysis is in many respects a crude tool; it does not facilitate identifying robust relationships between cluster membership and other potential properties of each data point.

To probe the relationship between our data and patterns of endangerment, we operationalised the state of the language in quantitative terms using census returns. We mapped the LSS enquiry points to census enquiry points and established the number and percentage of Gaelic speakers in each locality, using the census data collated by Duwe (2003). We utilised the 1891 census, as it was the closest to 1886, the average date of birth for LSS informants. In order to gauge the strength of Gaelic within the community, we used the percentage of Gaelic speakers at each point as our proxy for the state of the language at the time.

Apart from the demographic state of the language, a potential factor influencing the conservatism score is the sex of the informant. The Linguistic Survey of Scotland was conducted in a traditional dialectal framework targeting NORM informants, based on the assumption that males, at least in a traditional society, may be less open to linguistic changes (Chambers & Trudgill 1998: pp. 47, 61). Our results can be used to check that assumption.

Figure 6 shows the overall relationship between the share of Gaelic speakers in a locality and the aggregate conservatism score, by sex of the respondent, using a thin plate regression spline curve fit via the R package mgcv (Wood 2006). On average, as can be seen from the figure, this relationship is positive: a larger share of Gaelic speakers corresponds, overall, to a higher conservatism score. We can also see that the overall level of conservatism for male respondents is higher than for female respondents: this is confirmed by a two-sided t-test (t(154) = 2.768, p = .006329).

Figure 6

Overall relationship between demographics and the conservatism score.

Nevertheless, we observe a significant number of outliers in both directions: we find points with a low percentage of Gaelic speakers and a high conservatism score, and vice versa. For instance, point 81 Loch Don (Isle of Mull) is in the lowest quartile for percentage of Gaelic speakers, but in the highest quartile for conservatism; conversely, points 59 Ellenabeich (Isle of Seil), 76 Muirshearlich (in Lochaber, near Fort William), and 132 Polin (Assynt) are in the highest quartile for speaker percentage but in the lowest quartile for conservatism.

In order to further probe the relationship between informant sex, date of birth, demography, and space, we fit a range of non-linear regression models with conservatism as the dependent variable. We used generalised additive models implemented in the R package mgcv (Wood 2006), treating the share of Gaelic speakers and year of birth as smooths to account for any possible non-linearity in the effect. We also introduced a group-level coefficient (“random effect”, in this case an intercept) of registration district. The reasoning behind the introduction of district was that it could be used as a proxy for geographical proximity, allowing us to check for any spatial non-heterogeneity.

The best model is summarised in Table 2. Model comparison was conducted using likelihood ratio tests and the Akaike Information Criterion (more specifically second-order AIC, or AIC_c, as recommended by Burnham & Anderson 2004 for sample sizes as small as ours. For details on the model selection procedure and test results, see the R notebook in the Supplementary Material). In this model, the overall effect of demography is positive, in that larger percentages of Gaelic speakers result in a higher conservatism score. The district random effect is also included in the model, and it accounts for spatial heterogeneity. Specifically, models without this effect showed significant spatial autocorrelation in the distribution of residuals; that is to say, in models without the random effect of district there was spatial variation in how well the main effects were able to predict the conservatism at each point. In the best model, which does include the random effect of district, the distribution of residuals is much closer to the normal, and the residuals also showed a random spatial distribution.¹²

Figure 7 visualizes this spatial variation by district. It shows the LSS(G) points with the estimated random effect for the district to which the point belongs (see the R notebook for estimates of effects and confidence intervals). We can still observe an east-west cline, but the picture is less noisy than one based on raw data. This is partly because we have been able to control for some confounding variables, and partly because the grouping of points into districts is, by necessity, a pooling exercise. The clearer picture emerging in Figure 7 appears to indicate real diatopic variation, by exposing divisions that were less clear in the raw data: see in particular the visible differences between the northern and the southern parts of the Outer Hebridean chain, and the north-west mainland.

Figure 7

Estimated district random effect at each point.

To summarise our spatial analysis of the aggregate conservatism score, we observe a pronounced east-west cline in our data, whereby the western areas preserve the “Biblical Gaelic” morphological “ideal” to a greater extent than eastern ones. This distribution, perhaps unsurprisingly, shows a non-trivial relationship with the dynamics of language endangerment and language shift. Nevertheless, the relationship between the demographic context and morphological conservatism is not straightforward: while some areas represent the overall relationship between these two factors, other zones show greater or lesser conservatism than expected on the basis of the full data. Thus, there appears to be an interaction between geographic space and the speed with which language endangerment influences morphological developments, with different varieties implementing this relationship differently. In other words, language shift alone appears to be insufficient to explain the morphological effects visible in the overall east-west conservatism cline, and the data reveals a genuine diatopic signal.

3.5 Correlation analysis: Features

In this section, we show how quantitative analysis of survey data may allow us not only to answer specific questions related to the properties of individual Gaelic varieties and their place within the wider dialectological picture, but also to give insights into the underlying grammatical structure of the language. The morphological survey data is quite “shallow”, focusing on lexical allomorphy and surface morphophonology such as consonant mutation or vowel changes. It cannot be taken for granted that similar surface phenomena necessarily reflect identical structural processes (e.g. lenition across different grammatical contexts). Conversely, it is possible that the same structural process could be responsible for more than one surface phenomenon (e.g. gender agreement influencing a variety of surface phenomena). The latter point is strongly emphasised in Adger’s (2017) study of the syntactic mechanisms active in Gaelic language obsolescence. Adger argues that a single syntactic change – the loss of agreement features – produces visible effects in an entire set of apparently unrelated surface constructions, from the expression of possession, to the syntax of pronominal objects of non-finite verbs, to the structure of passive constructions. His argument focuses on the changes apparent under conditions of obsolescence in the East Sutherland variety described by Dorian (1978a): he suggests that the co-occurrence of all these “surface” changes in that variety reflects a single modification of the underlying grammar.

Here, we extend this thinking to a broader range of observed features. Following Adger (2017), we suggest that co-variation of two or more surface features deserves to be taken seriously as a potential indicator of the commonality of the grammatical process underlying them. The logic is simple: if grammatical change X leads to surface changes Y and Z, then varieties undergoing X should show both Y and Z, too.

To evaluate this proposition quantitatively, we conducted a correlation analysis of the features in the dataset. Once again, we treated missing returns as negative responses, to reflect the fact that both a lack of return and a negative response indicate non-adherence to the conservative ideal. Following this transformation, we calculated the correlation between every pair of features and tested the significance of each correlation coefficient. The correlation was calculated using Spearman’s ρ criterion, which is appropriate for our categorical data, since it is computed using rank order rather than treating 1 and 0 as values of a continuous variable.

The results of the calculations are presented in Figure 8. The figure presents the correlation matrix in graphical form. Only those cells where the correlation coefficient was significant at p < .01 are shown in the plot. The size of the circle indicates the absolute value of the correlation coefficient, as does the depth of the colour. Red hues indicate positive correlations, and blue hues indicate negative correlations. The features are ordered in the matrix based on the outcomes of a Ward agglomerative clustering procedure and the HCPC (hierarchical clustering on principal components) methodology. That clustering is also reflected in the rectangles, which delimit 20 clusters. This is done simply to better visualise which of the adjacent features do cluster together; no significance should be attached to the number of clusters.

Figure 8

Correlation plot of features.

This analysis succeeds in bringing out the internal structure of the data. Many of the features in the questionnaire correspond to a single grammatical phenomenon elicited in multiple contexts. For instance, we can observe that there is a strong correlation between the returns regarding the presence of a plural suffix in monosyllabic adjectives: the features na casan beag[a] (after a feminine 2nd declension noun), na balaich bheag[a] (after a masculine 1st declension noun), na cathraichean beag[a] (after a feminine 5th declension noun), and na sùilean glas[a] (after a feminine 3rd declension noun) are well correlated. This is expected, since the presence of this suffix should depend only on the number of syllables in the adjective itself, rather than the gender or declension class of the preceding noun.

That the algorithm is able to recover such hidden structure can serve as a sanity check to give us some confidence that the correlations it uncovers correspond to grammatical or lexical categories. Having established this, we can now proceed to examine several feature bundles more closely.

3.6 Discussion

As reported above, this work has two complementary strands: an aggregate analysis of diatopic morphological variation in Scottish Gaelic, which we have conducted partly as a methodological sanity check, and an attempt to infer deeper grammatical patterns through examining the behaviour of morphological features across varieties.

To gauge the patterns of diatopic variation, we used well-established dialectometrical methods such as edit distance, agglomerative clustering and multiple factor analysis. We also introduced a measure of morphological conservatism to examine the degree of individual survey points’ adherence to the established conservative norm. On the basis of this data, the Outer Hebrides – especially the southern isles such as the Uists and Barra – emerged as the most conservative region. Furthermore, a relatively strong east-to-west cline obtained, with innovative forms being increasingly more common towards the eastern periphery.

Although these findings confirm some modern lay expectations, in many ways they disagree with Jackson’s (1968) postulation of a more conservative “centre” and a more progressive and fragmented “periphery”. Rather, what we see here is relatively clear differentiation between the mainland – apart from isolated areas on the west coast and Lewis – and the more conservative Hebridean region. We also examined the hypothesis that the patterns of variation observed in the data are due to language attrition and obsolescence in the context of language minoritisation. Although it appears that language decline does play a role in the maintenance of conservative norms, a clear diatopic signal is also present in the data, undermining the opinion, previously expressed by some authorities, that Gaelic morphosyntax does not exhibit significant variation in space.

The results discussed in Section 3.5 extend the findings of Dorian (1973; 1977) and Adger (2017) beyond a single variety. Dialectometrical methods allow us to quantify whether the effects observed “up close” in the examination of a single dialect are found across a larger area, and hence whether the results can be said to hold true for the language at large.

A particular advantage of a quantitative approach is that it can leverage hidden structure within the data. Clustering algorithms consider not just the pairwise relationship between the features of interest, but also whether they are similar in how they relate to other features within the dataset. If several surface features reflect the same grammatical phenomenon, then it is straightforwardly predicted that these features should co-occur whenever the underlying grammatical phenomenon is present. A second, more subtle prediction, is that these co-occurring features should have a similar distribution vis-à-vis other features, again because their distribution should reflect the distribution of the underlying phenomenon. This is something that is difficult to verify within a single variety, but dialectometry provides a way of achieving this result.

In our dataset, these methods have allowed us to establish quite firmly the difference in behaviour between morphophonological exponents (e.g. consonant mutation and slenderisation), their behaviour in the context of a particular morphological category, and the morphological categories themselves. As our results show, we can in principle distinguish between three types of change:

• Wholesale loss of an exponent. We do not observe this with morphophonological phenomena such as mutation, but the patterning of the adjective plural suffix -a discussed in Section 3.5 can be interpreted in this way;

• Loss of the use of a particular exponent for a morphological category, without the loss of the category itself. We saw this with the loss of dative slenderisation without the loss of dative (Section 3.5.1), and the separate behaviour of lenition and slenderisation in the vocative (Section 3.5.2);

• Loss of the morphological category itself, which leads to the loss of all surface features associated with it. This, as we argued, is the case for grammatical gender: its loss leads to the loss of mutation in nouns after the article and adjectives after nouns.

The distinction between exponents and categories that we have pinpointed corresponds to that made by Dorian (1973; 1977) in her study of initial mutations in East Sutherland Gaelic. She distinguishes between having access to “a repertory of initial consonant choices” (including lenition and nasalisation), and the use of those consonant choices in different grammatical contexts.¹⁷

The findings underline the importance of considering change in the exponence of grammatical categories separately from change in the behaviour of the grammatical category itself. This is particularly important given the socioeconomic context of Gaelic as a minoritised, endangered language. As we have repeatedly noted, many of the changes observed in the language have been ascribed to language obsolescence (notably by Dorian 1973; 1978b, but contrast Hamp 1989). Non-compliance with the prescribed conservative ideal is, of course, also commonly interpreted in this context in lay discourses (Bell et al. 2014). However, as we have seen, the loss of a particular exponent such as consonant mutation or slenderisation does not necessarily imply the breakdown or loss of the underlying element of the grammatical system.

To summarise, then, our study has demonstrated how dialectometry can be useful not only for the study of diatopic, social, or diachronic variation within a set of varieties, but also in order to discover the underlying grammatical structure, in ways that are less accessible to traditional methods.