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Time-based data models and the Text Encoding Initiative’s guidelines for transcription of speech
(2005)
Frimer et al. (2015) claim that there is a linear relationship between the level of prosocial language and the level of public disapproval of US Congress. A re-analysis demonstrates that this relationship is the result of a misspecified model that does not account for first-order autocorrelated disturbances. A Stata script to reproduce all presented results is available as an appendix.
In a previous study published in Nature Human Behaviour, Varnum and Grossmann claim that reductions in gender inequality are linked to reductions in pathogen prevalence in the United States between 1951 and 2013. Since the statistical methods used by Varnum and Grossmann are known to induce (seemingly) significant correlations between unrelated time series, so-called spurious or non-sense correlations, we test here whether the statistical association between gender inequality and pathogens prevalence in its current form also is the result of mis-specified models that do not correctly account for the temporal structure of the data. Our analysis clearly suggests that this is the case. We then discuss and apply several standard approaches of modelling time-series processes in the data and show that there is, at least as of now, no support for a statistical association between gender inequality and pathogen prevalence.
It was recently suggested in a study published in Nature Human Behaviour that the historical loosening of American culture was associated with a trade-off between higher creativity and lower order. To this end, Jackson et al. generate a linguistic index of cultural tightness based on the Google Books Ngram corpus and use this index to show that American norms loosened between 1800 and 2000. While we remain agnostic toward a potential loosening of American culture and a statistical association with creativity/order, we show here that the methods used by Jackson et al. are neither suitable for testing the validity of the index nor for establishing possible relationships with creativity/order.
Developments within the field of Second Language Acquisition (SLA) have meant that scholars are increasingly engaging with corpora and corpus-based resources, providing a source of “‘authentic’ language” to learners and educators (Mitchell 2020: 254), and contributing to “state-of-the-art research methodologies” (Deshors and Gries 2023: 164). However, there are areas in which progress can still be made, particularly in the area of metadata, such as information about the speaker and contexts of the language use, as well as increased variety in the text types and genres of corpora used to develop SLA materials (Paquot 2022: 36). This post discusses one such possibility for increasing the variety of text types and providing a rich source of authentic language that can be used to create engaging SLA materials, particularly for young people learning German, namely the use of the NottDeuYTSch corpus (to download the corpus in a variety of formats, see Cotgrove 2018).
Speech islands are historically and developmentally unique and will inevitably disappear within the next decades. We urgently need to preserve their remains and exploit what is left in order to make research on language-in-contact and historical as well as current comparative language research possible.
The Archive for Spoken German (AGD) at the Institute for German Language collects, fosters and archives data from completed research projects and makes them available to the wider research community.
Besides large variation corpora and corpora of conversational speech, the archive already contains a range of collections of data on German speech minorities. The latter will be outlined in this chapter. Some speech island data is already made available through the personal service of the AGD, or the database of spoken German (DGD), e.g. data on Australian German, Unserdeutsch, or German in North America. Some corpora are still being prepared for publication, but still important to document for potentially interested research projects. We therefore also explain the current problems and efforts related to the curation of speech island data, from the digitization of recordings and the collection of metadata, to the integration of transcriptions, annotations and other ways of accessing and sharing data.
In a recent paper published in the Journal of Language Evolution, Kauhanen, Einhaus & Walkden (KEW) challenge the results presented in one of my papers (Koplenig, Royal Society Open Science, 6, 181274 (2019)), in which I tried to show through a series of statistical analyses that large numbers of L2 (second language) speakers do not seem to affect the (grammatical or statistical) complexity of a language. To this end, I focus on the way in which the Ethnologue assesses language status: a language is characterised as vehicular if, in addition to being used by L1 (first language) speakers, it should also have a significant number of L2 users. KEW criticise both the use of vehicularity as a (binary) indicator of whether a language has a significant number of L2 users and the idea of imputing a zero proportion of L2 speakers to non-vehicular languages whenever a direct estimate of that proportion is unavailable. While I recognise the importance of post-publication commentary on published research, I show in this rejoinder that both points of criticism are explicitly mentioned and analysed in my paper. In addition, I also comment on other points raised by KEW and demonstrate that both alternative analyses offered by KEW do not stand up to closer scrutiny.
Less than one percent of words would be affected by gender-inclusive language in German press texts
(2024)
Research on gender and language is tightly knitted to social debates on gender equality and non-discriminatory language use. Psycholinguistic scholars have made significant contributions in this field. However, corpus-based studies that investigate these matters within the context of language use are still rare. In our study, we address the question of how much textual material would actually have to be changed if non-gender-inclusive texts were rewritten to be gender-inclusive. This quantitative measure is an important empirical insight, as a recurring argument against the use of gender-inclusive German is that it supposedly makes written texts too long and complicated. It is also argued that gender-inclusive language has negative effects on language learners. However, such effects are only likely if gender-inclusive texts are very different from those that are not gender-inclusive. In our corpus-linguistic study, we manually annotated German press texts to identify the parts that would have to be changed. Our results show that, on average, less than 1% of all tokens would be affected by gender-inclusive language. This small proportion calls into question whether gender-inclusive German presents a substantial barrier to understanding and learning the language, particularly when we take into account the potential complexities of interpreting masculine generics.