Suchergebnisse

Campbell reviews 'Class Notes: Posing as Politics and Other Thoughts on the American Scene' and 'Stirrings in the Jug: Black Politics in the Post-Segregation Era' by Adolph Reed Jr, and 'Without Justice for All: The New Liberalism and Our Retreat From Racial Equality' edited by Adolph Reed Jr.

This article examines ethics in work organization and in academic, particularly Critical Management Studies, research. It is centred on empirical data exploring the actions of three employees of a higher education institution who variously failed to resist and/or colluded in the sex discrimination of a colleague. We bring ethics to bear in our analysis of these data in three ways. First, reflecting upon our own methodology, we highlight the difficulties of balancing competing ethical responsibilities when engaging in critical research in contexts defined by adversarial relationships. Second, we highlight how research subjects, who we interpret as exercising problematic agency, draw upon discourses of care, friendship and responsibility to discursively construct their behaviour as moral. Third, drawing upon feminist theory, we reflect upon the ethical warrant of academic critiques of research subjects' agency. Our analysis raises unsettling implications both for the ethics of Critical Management Studies research and for the function of ethics in organizations. We end by being as concerned by the capacity of ethical discourse to enable and legitimize discrimination as we are reassured by its utility to enable us to discriminate right from wrong behaviour in organizations.

In her book 'Discriminating Data' (2021), Wendy Chun reveals how polarization is a goal — not an error — within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Hito Steyerl and Wendy Chun will discuss how can people release themselves from the vice-like grip of discriminatory data and consider alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks. Wendy Hui Kyong Chun is the Canada 150 Research Chair in New Media at Simon Fraser University, and leads the Digital Democracies Institute which was launched in 2019. She studied Systems Design Engineering and English Literature and is author of Control and Freedom (2006), Programmed Visions (2011), and Updating to Remain the Same (2016). Hito Steyerl works as a filmmaker, philosopher, and cultural critic. Her work takes the form of essays, lectures, installations, video, and photography. She is professor for experimental film and video and the co-founder of the Research Center for Proxy Politics at the Berlin University of the Arts. ; Discriminating Data , discussion, ICI Berlin, 15 December 2021, video recording, mp4, 37:51

AbstractA non‐discriminating criterion is defined as a criterion where the decision‐maker is indifferent among the alternatives. One would therefore expect the final rank order of the alternatives not to be affected by removing it. A previously published paper by Finan and Hurley (Comput. Oper. Res. 2002; 29: 1025–1030) showed that in the analytic hierarchy process removing such a criterion from a multilevel hierarchy can reverse rank. In this paper, we offer an explanation of this particular rank reversal phenomenon and show how it can be avoided. We do this by taking into account that there is a link between the normalization and weighting processes, which suggests adjusting appropriate weights when removing criteria. Further, we discuss whether a non‐discriminating criterion should be removed in the first place. Copyright © 2009 John Wiley & Sons, Ltd.

We consider the problem of choosing between rival statistical models that are non-nested in terms of their functional forms. We assess the ability of two tests, one parametric and one distribution-free, to discriminate between such models. Our Monte Carlo simulations demonstrate that both tests are, to varying degrees, able to discriminate between strategic and non-strategic discrete choice models. The distribution-free test appears to have greater relative power in small samples.