51,362 research outputs found

    Should there be more women in science and engineering?

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    Many people hold this truth to be self-evident, that there should be more female students in science and engineering. Typical arguments include possible benefits to women, possible benefits to the economy, and the unfairness of the current female under-representation. However, these justifications are never explicitly and thoroughly presented. Clearly stating and scrutinizing them, we show that they in fact have logical flaws. When made consistent, these arguments do not unconditionally justify enrolling more women in scientific disciplines. In particular, what women want must be taken into account. Outreach programs towards K-12 girls must therefore purport to allow them to choose a field freely, rather than try to draw as many of them to scientific disciplines as possible. This change of mindset must be accompanied by a close examination of the purpose and effects of these programs

    Choosing effective methods for design diversity - How to progress from intuition to science

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    Design diversity is a popular defence against design faults in safety critical systems. Design diversity is at times pursued by simply isolating the development teams of the different versions, but it is presumably better to "force" diversity, by appropriate prescriptions to the teams. There are many ways of forcing diversity. Yet, managers who have to choose a cost-effective combination of these have little guidance except their own intuition. We argue the need for more scientifically based recommendations, and outline the problems with producing them. We focus on what we think is the standard basis for most recommendations: the belief that, in order to produce failure diversity among versions, project decisions should aim at causing "diversity" among the faults in the versions. We attempt to clarify what these beliefs mean, in which cases they may be justified and how they can be checked or disproved experimentally

    Is diversity good?

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    Prominent ethical and policy issues such as affirmative action and female enrollment in science and engineering revolve around the idea that diversity is good. However, even though diversity is an ambiguous concept, a precise definition is seldom provided. We show that diversity may be construed as a factual description, a craving for symmetry, an intrinsic good, an instrumental good, a symptom, or a side effect. These acceptions differ vastly in their nature and properties. The first one cannot lead to any action and the second one is mistaken. Diversity as intrinsic good is a mere opinion, which cannot be concretely applied; moreover, the most commonly invoked forms of diversity (sexual and racial) are not intrinsically good. On the other hand, diversity as instrumental good can be evaluated empirically and can give rise to policies, but these may be very weak. Finally, symptoms and side effects are not actually about diversity. We consider the example of female enrollment in science and engineering, interpreting the various arguments found in the literature in light of this polysemy. Keywords: ethics, policy, higher education, female students, minority students, affirmative actionComment: 7 page

    Responsible Data Governance of Neuroscience Big Data

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    Open access article.Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of “responsible data governance,” applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP)

    [Subject benchmark statement]: computing

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    Furthering alternative cultures of valuation in higher education research

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    The value of higher education is often implicit or assumed in educational research. The underlying and antecedent premises that shape and influence debates about value remain unchallenged which perpetuates the dominant, but limiting, terms of the debate and fosters reductionism. I proceed on the premise that analyses of value are not self–supporting or self-referential but are embedded within prevailing cultures of valuation. I contend that challenging, and providing alternatives to, dominant narratives of higher education requires an appreciation of those cultures. I therefore highlight some of the existing cultures of valuation and their influence. I then propose Sayer’s concept of lay normativity as a culture of valuation and discuss how it translates into the practices of research into higher education, specifically the practice of analysis. I animate the discussion by detecting the presence of lay normativity in the evaluative space of the capability approach
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