Présentation de l’ouvrage. Les relations dans la vie quotidienne sont troublées par des conflits qui, si la violence physique est écartée, conduisent à formuler des critiques auxquelles répondent des justifications. En tirant parti à la fois de la philosophie politique et de l’étude sociologique de disputes, notamment au sein d’organisations, les auteurs montrent que ces critiques et ces justifications ne sont pas seulement circonstancielles mais qu’elles expriment un sens commun de la justice. Ils mettent au jour les règles que doivent suivre ces critiques et ces justifications pour être jugées recevables. Elles composent une grammaire inscrite à la fois dans le langage et dans des dispositifs matériels qui l’ancrent dans la réalité. Cette grammaire du désaccord et de l’accord est pluraliste : elle permet aux personnes de prendre appui sur différents ordres d’évaluation en fonction de la situation dans laquelle elles se trouvent plongées. Elle contribue en outre à réduire la tension entre, d’une part, ces ordres de grandeur et, de l’autre, un principe d’égalité aspirant à une humanité commune. Loin d’être un relativisme, ce pluralisme offre des ressources dont les personnes peuvent se saisir pour résister à la menace constante de domination.
About the book. Relationships in everyday life are troubled by conflicts which, if physical violence is ruled out, lead to criticism to which justifications are given. Drawing on both political philosophy and the sociological study of disputes, particularly within organizations, the authors show that these criticisms and justifications are not merely circumstantial but express a common sense of justice. They reveal the rules that these criticisms and justifications must follow in order to be considered admissible. They compose a grammar inscribed both in language and in material devices that anchor it in reality. This grammar of disagreement and agreement is pluralistic: it allows people to draw on different orders of evaluation depending on the situation in which they find themselves. It also contributes to reducing the tension between, on the one hand, these orders of magnitude and, on the other, a principle of equality aspiring to a common humanity. Far from being a relativism, this pluralism offers resources that people can seize to resist the constant threat of domination.
Kölner Zeitschrift für Soziologie und Sozialpsychologie. Online first
Abstract. Digital process data are becoming increasingly important for social science research, but their quality has been gravely neglected so far. In this article, we adopt a process perspective and argue that data extracted from socio-technical systems are, in principle, subject to the same error-inducing mechanisms as traditional forms of social science data, namely biases that arise before their acquisition (observational design), during their acquisition (data generation), and after their acquisition (data processing). As the lack of access and insight into the actual processes of data production renders key traditional mechanisms of quality assurance largely impossible, it is essential to identify data quality problems in the data available—that is, to focus on the possibilities post-hoc quality assessment offers to us. We advance a post-hoc strategy of data quality assurance, integrating simulation and explorative identification techniques. As a use case, we illustrate this approach with the example of bot activity and the effects this phenomenon can have on digital process data. First, we employ agent-based modelling to simulate datasets containing these data problems. Subsequently, we demonstrate the possibilities and challenges of post-hoc control by mobilizing geometric data analysis, an exemplary technique for identifying data quality issues.
Extract: “To derive a systematic overview of the possible phenomena, sources, and mechanisms of errors, we employ a process perspective. We understand data production as processes emerging from the genuine interplay of social and technological entities. The systematic conception of the data-generating process and its accompanying errors have been examined both in the context of a process-oriented theory of survey research (Bachleitner et al. 2010) and as a “statistical chain”—that is, as a relational interplay in which different entities, objects, practices, and situations jointly generate data (see Desrosières 2009; Diaz-Bone 2018; Diaz-Bone et al. 2020, p. 319). Problems with data quality (as well as adequate interpretability) arise from the inconsistency of conventions between the different links in the chain of data production. Interviewers, data managers, statisticians, and recipients will differ in their data-related knowledge, definitions, implicit assumptions, practical choices, and their conceptions about the (realist or constructivist) status of the data and its constructs. On this analytical basis, successful attempts have been made, for survey data, to trace the process of data production from start to finish, to theoretically grasp the mechanisms of distortion, and to thereby make them accessible for investigation and, eventually, correction.” (p. 4)