Suchergebnisse

he COLOSSUS Project brought together four Scottish Further Education Col leges (Adam Smith, Forth Valley , Stevenson College Edinburgh and Stow), the Western Colleges Consortium representing four colleges in the South West of England and the University of Strathclyde to pursue two broad aims; to develop online learning materials relevant to the Social Sciences and to develop a workflow model for the continued development of online learning materials within the institutions that was sustainable beyond the period of external funding. To this end the partners not only worked on the creation of the learning materials but did so in a self - reflective manner examining the whole development process including ;  organisational and administrative arrangement s surrounding the development  the workflow processes undertaken by the various staff engaged in the production  the roles performed and the levels of engagement exhibited by staff  the technologies employed and their effectiveness  third party materials available to the development and the use to which they were put Through the life of the p roject the Scottish FE partners developed forty four learning objects in five subjects; Economics, Women's Studies, History, Politics and Psychology. These objects were constructed as standalone learning experiences comprising explicit learning objectiv es, activities, integral assessment and further documentation for prospective tutors. The objects were packaged according to the SCORM 1.2 specification to interoperate with SCORM - compliant VLEs. These objects are now available from JORUM. Opportunities for reuse were investigated using some of these learning objects. Central to this investigation was an assessment of the usefulness to English curricula of materials developed to meet the requirements of the Scottish Qualifications Authority Higher Nati onal Social Science framework; the results of which were generally very positive. Another aspect of the investigation was to explore opportunities for reuse with additional materials and in different formats; and a series of practical guides were develope d . These case studies are available from the COLOSSUS Project website. 1 However it was the examination of and reflection upon the workflow processes that will likely prove the most valuable for the partners engaged in the development of the learning obje cts. Individual institutional the Workflow Model Case Studies reflect not only the individual experiences of the colleges, but also reveal common challenges and responses. 2 As a result of this exercise partner colleges have revised and adapted their appr oaches to online learning development to become more structured, streamlined and ultimately sustainable. The result is the COLOSSUS Sustainable Development Model , a guide to workflow process management and development practices that facilitate the ongoing development of online learning materials without the need for additional external funding. This model has been adopted and adapted by partners to support their own development

Recent years have seen a surge in psychological research on the relationship between political ideology (particularly conservatism) and cognition, affect, behaviour, and even biology. Despite this flurry of investigation, however, there is as yet no accepted, validated, and widely used multi-item scale of conservatism that is concise, that is modern in its conceptualisation, and that includes both social and economic conservatism subscales. In this paper the 12-Item Social and Economic Conservatism Scale (SECS) is proposed and validated to help fill this gap. The SECS is suggested to be an important and useful tool for researchers working in political psychology.

Recent years have seen a surge in psychological research on the relationship between political ideology (particularly conservatism) and cognition, affect, behaviour, and even biology. Despite this flurry of investigation, however, there is as yet no accepted, validated, and widely used multi-item scale of conservatism that is concise, that is modern in its conceptualisation, and that includes both social and economic conservatism subscales. In this paper the 12-Item Social and Economic Conservatism Scale (SECS) is proposed and validated to help fill this gap. The SECS is suggested to be an important and useful tool for researchers working in political psychology.

Abstract
Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.