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Descriptive representation is commonly understood as the proportion of women or racial minorities in an institution. While useful, this approach is limited in its ability to capture intersectional identities, less visible characteristics, and the extent to which particular characteristics are more or less central to one's identity. Traditional approaches have raised concerns about essentialism—"the assumption that members of certain groups have an essential identity that all members of that group share" (Mansbridge 1999, 637). This assumption can lead to faulty logic—for example, that any woman can represent all women. Traditional approaches have also focused on visible characteristics, rather than shared experiences. These limitations affect not only who counts as a descriptive representative, but also our ability to assess which descriptive representatives will be most likely to contribute to substantive and symbolic representation.

Die Entwicklungen im Bereich der Automatisierung hin zu steigender Datendurchgängigkeit in Kombination mit der Verfügbarkeit frei zugänglicher Datenanalyseplattformen
 
und -algorithmen erlauben neue Ansätze zur kontinuierlichen Verbesserung von Produktionsprozessen. Auch etablierte Vorgehensweisen wie Six Sigma können und müssen in diesem Rahmen weitergedacht und angereichert werden. Folglich gilt es, die klassische, hauptsächlich deskriptive Herangehensweise von Six Sigma um relevante Methoden und Algorithmen aus den Bereichen Data Mining, Machine Learning und künstliche Intelligenz zu erweitern. Die klassische Six Sigma Ausbildung bietet für diesen Wandel gute Voraussetzungen, die es auszubauen und anzupassen gilt.
 
The increasing data availability in combination with open source data analysis platforms and algorithms pave the way for new ways of operationalizing continuous improvement tasks in the field of production processes. Even established approaches like Six Sigma need to be enhanced and enriched in this context. Consequently, the classical and more descriptive nature of Six Sigma should consider relevant methods and algorithms out of the field of data mining, machine learning and artificial intelligence. The classical Six Sigma training provides a good basis for this change to broaden the Six Sigma scope and its toolbox.

SUMMARYThe higher degree of description is reached when a field becomes amenable to the use of the axiomatic method. In economics this state has been reached only recently in the establishment of a numerical utility. In general, it is difficult to know what to describe and how to measure; both require precise concepts. Current economic descriptions lack both very often, in particular the whole area of psychological factors, decisions, etc., escape the essentially physical descriptions of economic phenomena.Good predictions are the ultimate test of theory but also false theories often have a surprising power to predict. Thus, this matter is much more complicated than normally assumed.Even descriptive theories can be interpreted as 'normative' provided they are convincing to the user. The implied norm is often a prohibition, as for example, in the statement that one should not try to build a perpetuum mobile. If a state of society is commonly accepted which sanctions private property, then the norm follows that one ought not to steal. This is different from normative value statements that derive solely from other norms.

How well do governments represent the societies they serve? A key aspect of this question concerns the extent to which leaders reflect the demographic features of the population they represent. To address this important issue in a systematic manner, we propose a unified approach for measuring descriptive representation. We apply this approach to newly collected data describing the ethnic, linguistic, religious, and gender identities of over fifty thousand leaders serving in 1,552 political bodies across 156 countries. Strikingly, no country represents social groups in rough proportion to their share of the population. To explain this shortfall, we focus on compositional factors—the size of political bodies as well as the number and relative size of social groups. We investigate these factors using a simple model based on random sampling and the original data described above. Our analyses demonstrate that roughly half of the variability in descriptive representation is attributable to compositional factors.

This study is a descriptive study of causative constructions in Bassa language. It is a language classified as belonging to the Western Kru of Benue Congo (Blench, & Williamson, 2000:25; Crozier and Blench, 1992:32). Causative construction implies an expression where the caused event is depicted as taking place because someone does something or something happens, that is, if x hadn't happened, y wouldn't have happened. The process is characterized by two events such that one occurs at t1 and another at t2, and the occurrence of the first is responsible for the second event. Data for the study were sourced by a customized checklist, my native intuition as native speaker, and some written texts in Bassa. The work discovered that morphologically derived causative verbs could come from a verb or an adjective stem and in each case; it is characterized by transitivizing the derived verb with the resultant effect of increasing the argument by one to the basic structure and transformation of the arguments. In this case, the basic subject moves to the object position and the applied argument, that is, the causer argument becomes the subject of the derived mono-clausal structure. This work also discovered that when these processes occur, the applied subject is focalized, becomes the privileged argument and displaces the inherent subject and moves it below the predicate and the derived mono-clausal sentence from the complex sentence is economized.UJAH: Unizik Journal of Arts and Humanities vol 14 (2) 2013

AbstractDrawing on the descriptive representation literature, we argue that religious identity is a social identity similar to gender or race, which leads a person to feel represented by someone who shares their religious identity. We argue that religious identity motivates approbation for public officials that is distinct from partisanship. We find that constituents who share the religious identity of their congressional representatives are significantly more likely to approve of their representative's performance in office. In addition, those who share a religious identity with President Obama are more trusting of him; particularly among those for whom religion is important. Finally, we find that shared religious identity moderates the relationship between partisanship and trust in the President. All else equal, Republicans who share a religious identity with President Obama are 500% more likely to trust him than a Republican who does not.

DOC Dairy Partners B.V. v Dairy Partners Limited