Eckert, P. (1997). Age as a sociolinguistic variable. In F. Coulmas (Ed.), The handbook of sociolinguistics (pp. 151–167). Blackwell.

Riverin-Coutlée, J., & Harrington, J. (2022). Phonetic change over the career: A case study. Linguistics Vanguard, 8(1), 41–52. https://doi.org/10.1515/lingvan-2021-0122

Sankoff, G. (2018). Language change across the lifespan. Annual Review of Linguistics, 4(1), 297–316.

Labov, W. (1966). The social stratification of English in New York City. Center for Applied Linguistics.

Labov, W. (2001). Principles of linguistic change, vol. II: Social factors. Wiley-Blackwell.

Warner, W. L., & Lunt, P. S. (1942). The social life of a modern community. Yale University Press.

Warner, W. L., Meeker, M., & Eells, K. (1949). Social class in America. Science Research Associates.

Prediger, A. (2016). Erwerb und Gebrauch alemannischer Dialektmerkmale durch russische Muttersprachler [Unpublished Dissertation]. Universität Freiburg.

Wirtz, M. A. (under review). On the relationship between complexity of primary occupation and L2 varietal behavior in adult migrants in Austria.

Leemann, A., Derungs, C., & Elspaß, S. (2019). Analyzing linguistic variation and change using gamification web apps: The case of German-speaking Europe. PLOS ONE, 14(12), 1–29. https://doi.org/10.1371/journal.pone.0225399

Wirtz, M. A., Pickl, S., Niehaus, K., Elspaß, S., & Möller, R. (under review). Reconciling the social and spatial: An apparent-time analysis based on variation intensity.

Occupational status is potentially one of the most pervasive factors throughout adulthood. As Eckert (1997:167) put it, work-related landmarks in adulthood, e.g., a person’s first full-time employment, promotions, job loss, career changes, retirement, etc., are “life experiences that give age meaning” and can critically influence the way an individual draws on language as a communicative tool. For example, Riverin-Coutlée and Harrington’s (2022) investigation of phonetic flexibility in adulthood evinced that the individual career path may influence changes in phonetic characteristics, the hypothesis being that such salient external life-course transitions put sufficient pressure on speakers’ linguistic systems to outweigh a natural tendency towards (phonetic) stability during adulthood and thus facilitate linguistic flexibility and plasticity across the lifespan (e.g., Sankoff, 2018). Whereas sociolinguists have traditionally drawn on scales building on occupational factors, e.g., in operational measures of class-related, economic variables (e.g., Labov, 1966, 2001; Warner & Lunt, 1942; Warner et al., 1949), attempts to tap into different dimensions of the primary occupation and how these influence patterns of variation are comparatively lacking (Prediger, 2016; Wirtz, under review).

In this talk, we draw on crowdsourcing data collected for the Atlas zur deutschen Alltagssprache (AdA, i.e., Atlas of Colloquial German; Elspaß & Möller, 2003–). In the AdA, localities are not predefined, and participation is not constrained by any social parameters, which facilitates a more realistic and comprehensive portrait of colloquial language variation, but necessarily results in socially very diverse datasets (e.g., Leemann et al., 2019). In order to examine the extent to which primary occupation impacts on patterns of language variation in colloquial German, we advocate for a multi-dimensional approach to occupation based on the Dictionary of Occupational Titles (DOT; U.S. Department of Labor, 1977). The goal herewith is to take a multi-factorial perspective to communicative, data-related, and manual requirements of an occupation – i.e., occupational complexity with people, data, and things – in order to circumvent using arbitrary categories or thresholds as factors that function as proxies for occupation (Wirtz, under review). Specifically, we use occupational complexity to explore the effects of occupation on variation intensity, which quantifies the degree or intensity of variation based on the relative frequencies of each variant of a variable for each AdA informant, aggregated over a total of 62 variables (Wirtz et al., under review).

Using Bayesian multilevel models, we found that informants with higher OCCUPATIONAL COMPLEXITY WITH PEOPLE and THINGS were predicted to evince lower variation intensity (i.e., report fewer variants). However, the model indicated no effect of COMPLEXITY WITH DATA on informants’ variation intensity. As concerns the statistical interaction effect between COMPLEXITY WITH PEOPLE * THINGS, the model only predicted informants’ variation intensity to increase when COMPLEXITY WITH PEOPLE and COMPLEXITY WITH THINGS were at ceiling levels. We discuss these findings against a variationist backdrop and present additional scales that can meaningfully be combined with occupational complexity (e.g., occupational satisfaction) in order to glean more nuanced insights into the relationship between patterns of variation and occupational metrics.

Modeling Context and the Individual