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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.
This paper presents a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, that is their ability to construct and manage in-groups and out-groups by including/excluding the audience and/or non-speech act participants in reference to groups that also include the speaker. We apply and test our schema on pronoun instances in speeches taken from the German parliament. The speeches cover a time period from 2017-2021 and comprise manual annotations for 3,126 sentences. We achieve high inter-annotator agreement for our new schema, with a Cohen’s κ in the range of 89.7-93.2 and a percentage agreement of > 96%. Our exploratory analysis of in/exclusive pronoun use in the parliamentary setting provides some face validity for our new schema. Finally, we present baseline experiments for automatically predicting clusivity in political debates, with promising results for many referential constellations, yielding an overall 84.9% micro F1 for all pronouns.
The debate on the use of personal data in language resources usually focuses — and rightfully so — on anonymisation. However, this very same debate usually ends quickly with the conclusion that proper anonymisation would necessarily cause loss of linguistically valuable information. This paper discusses an alternative approach — pseudonymisation. While pseudonymisation does not solve all the problems (inasmuch as pseudonymised data are still to be regarded as personal data and therefore their processing should still comply with the GDPR principles), it does provide a significant relief, especially — but not only — for those who process personal data for research purposes. This paper describes pseudonymisation as a measure to safeguard rights and interests of data subjects under the GDPR (with a special focus on the right to be informed). It also provides a concrete example of pseudonymisation carried out within a research project at the Institute of Information Technology and Communications of the Otto von Guericke University Magdeburg.
KonsortSWD ist das NFDI Konsortium für die Sozial-, Verhaltens-, Bildungs- und Wirtschaftswissenschaften. Für die äußerst vielfältigen Datentypen und Forschungsmethoden bauen die Beteiligten im Rahmen der NFDI eine bereits bestehende Forschungsdateninfrastruktur aus und ergänzen neue integrierende Dienste. Basis sind die heute 41 vom Rat für Sozial- und Wirtschaftsdaten akkreditierten Forschungsdatenzentren (FDZ). FDZ sind Spezialsammlungen zu jeweils spezifischen Forschungsdaten, z.B. aus der qualitativen Sozialforschung, und können so Forschende auf Basis einer ausführlichen Expertise zu diesen Daten beraten. Neben der Unterstützung der FDZ baut KonsortSWD auch neue Dienste in den Bereichen Datenproduktion, Datenzugang und Technische Lösungen auf.
Based on conference reports and minutes, archive material and official documents, the article seeks to explore the way in which the promotion of women’s sports and of women in leadership positions became an important part of the sport policy of two major organizations involved in European sport cooperation: the Council of Europe and the European Sport Conference. During first and modest discussions in the 1960s and 1970s it constituted a rather paternalistic project. Also, it was based on the assumption of an essential difference between men and women concerning the need for participation in sport. This only changed since the beginning of the 1980s when women took the course in their own hands, challenged the underlying assumptions and created new networks of cooperation.
Sometimes legal scholars get relevant but baffling questions from laypersons like: “The reference to a work is personal data, so does the GDPR actually require me to anonymise it? Or, as my voice data is personal data, does the GDPR automatically give me access to a speech recognizer using my voice sample? Or, can I say anything about myself without the GDPR requiring the web host to anonymise or remove the post? What can I say about others like politicians? And, what can researchers say about patients in a research report?” Based on these questions, the authors address the interaction of intellectual property and data protection law in the context of data minimisation and attribution rights, access rights, trade secret protection, and freedom of expression.
Twitter data is used in a wide variety of research disciplines in Social Sciences and Humanities. Although most Twitter data is publicly available, its re-use and sharing raise many legal questions related to intellectual property and personal data protection. Moreover, the use of Twitter and its content is subject to the Terms of Service, which also regulate re-use and sharing. This extended abstract provides a brief analysis of these issues and introduces the new Academic Research product track, which enables authorized researchers to access Twitter API on a preferential basis.
Privacy in its many aspects is protected by various legal texts (e.g. the Basic Law, Civil Code, Criminal Code, or even the Law on Copyright in artistic and photographic works (KunstUrhG), which protects image rights). Data protection law, which governs the processing of information about individuals (personal data), also serves to protect their privacy. However, some information referring to the public sphere of an individual’s life (e.g. the fact that X is a mayor of Smallville) may still be considered personal data (see below), and as such fall within the scope of data protection rules. In this sense, data protection laws concern information that is not private.
Therefore, privacy and data protection, although closely related, are distinct notions: one can violate someone else’s privacy without processing his or her personal data (e.g. simply by knocking at one’s door at night, uninvited), and vice versa: one can violate data protection rules without violating privacy.
The following handouts focus exclusively on data protection rules, and specifically on the General Data Protection Regulation (GDPR). However, please keep in mind that compliance with the GDPR is not the only aspect of protecting privacy of individuals in research projects. Other rules, such as academic ethics and community standards (such as CARE) also need to be observed.