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    <title>STORRE Collection: Electronic copies of Management, Work and Organisation research reports.</title>
    <link>http://hdl.handle.net/1893/2691</link>
    <description>Electronic copies of Management, Work and Organisation research reports.</description>
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        <rdf:li rdf:resource="http://hdl.handle.net/1893/37866" />
        <rdf:li rdf:resource="http://hdl.handle.net/1893/37407" />
        <rdf:li rdf:resource="http://hdl.handle.net/1893/33179" />
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    <dc:date>2026-10-04T08:26:05Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/37866">
    <title>Scotland's Circular Economy Practices Ecosystem</title>
    <link>http://hdl.handle.net/1893/37866</link>
    <description>Title: Scotland's Circular Economy Practices Ecosystem
Author(s): Amos, Simon; Hruskova, Michaela
Abstract: First paragraph:  Scotland has a progressive reputation in the area of circular economy but the “Circularity Gap Report Scotland 2022” found that Scotland’s economy is only 1.3% ‘circular’.  Given the scale of contribution that a more circular economy can make to tackling climate change and other environmental and social issues, it is vital to understand why the current level of activity is not translating into a greater impact on the Scottish economy. This project therefore seeks to answer the question of ‘What affects the uptake of Circular Economy Practices by businesses in the Scottish Economy and why?’</description>
    <dc:date>2024-08-08T00:00:00Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/37407">
    <title>Generative AI &amp; Journalism: Mapping the Risk Landscape. Research Report</title>
    <link>http://hdl.handle.net/1893/37407</link>
    <description>Title: Generative AI &amp; Journalism: Mapping the Risk Landscape. Research Report
Author(s): Galanos, Vasileios; Jones, Bronwyn
Abstract: This research report explores how the growing use of generative AI across society poses risks for journalism as an industry, an institution, a practice and a product; for the wider information commons to which it contributes; and ultimately for people and society. Qualitative insights from interviews and a survey with a range of experts suggest that GenAI is amplifying many pre-existing long-standing challenges (e.g. business model and disruption, problematic business practices, information disorder, trust destabilisation). There was a strong appetite for intervention to mitigate such risks (e.g. by demanding greater transparency from AI companies, developing new professional and public literacies, devising new standards and strengthening public policy responses and funding).</description>
    <dc:date>2024-12-01T00:00:00Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/33179">
    <title>Rural policing in Scotland: measuring and improving public confidence, in Scottish Institute for Policing Research Annual Report for 2017/18</title>
    <link>http://hdl.handle.net/1893/33179</link>
    <description>Title: Rural policing in Scotland: measuring and improving public confidence, in Scottish Institute for Policing Research Annual Report for 2017/18
Author(s): Wooff, Andrew; Hail, Yvonne
Abstract: Maintaining and improving public confidence is a key part of Policing Strategy 2026 (Police Scotland, 2017). The strategy notes that ‘public confidence [is] a key measure of our performance’ (p33) and that a ‘broader understanding of public confidence’ (p57) is vital for maintaining and improving policing (Police Scotland, 2017), highlighting the link between public confidence and broader legitimacy and accountability of policing. A range of academic literature also points to the importance of public confidence for understanding police legitimacy and accountability (Jackson et al., 2012; Tyler, 2004). Public confidence, is used as a key measure of how the police are perceived to be doing, with the Scottish Government releasing statistics annually on ‘confidence with the police’. The diverse geography of Scotland and local variation in policing styles makes it important to understand the variation in public confidence across different community types and different geographical locations.</description>
    <dc:date>2018-09-01T00:00:00Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/33127">
    <title>D2 2 Methodology-and-Conceptual-framework</title>
    <link>http://hdl.handle.net/1893/33127</link>
    <description>Title: D2 2 Methodology-and-Conceptual-framework
Author(s): Hail, Yvonne; McQuaid, Ronald
Abstract: This document constitutes Deliverable D2.2 ‘Methodology and conceptual framework’ in the framework of the project titled ‘Revealing fair and actionable knowledge from data to support women’s inclusion in transport systems.’ (Project Acronym: DIAMOND; Grant Agreement No 824326). The Deliverable D2.2 aims to define a methodology and a conceptual framework for the overall project by following the next three different tasks according with the DoA:  - Task 2.2 ‘DIAMOND fairness concept definition’ produce a definition of fairness to assess the level of fair inclusion of a transport operator and take action to generate more inclusive transport system. A detailed definition of fairness for each use case will be produced. - Task 2.3 ‘DIAMOND methodology design and development’. This is a very important task due to it clearly define the methodology followed in the project to assess and favour women inclusion in the transport sector. A good definition of the methodology, including the data collection, data analysis, and the development of the Toolbox, will be crucial to lead to the results expected in the definition of the use-cases. - Task 2.4 ‘Definition of the conceptual framework’ will set a uniform vision for the following WPs to accomplish the objectives of each use-case.  The deliverable introduces the goal per each of the use cases addressed by the project based on the needs identified through a study of the state of the art. These goals were already presented in the D2.1.   After a deep analysis to apply the fairness concept to the transport system, a bottom-up (through the Focus group performed at Dublin, Paris and Warsaw) and an up-bottom (through literature review) approach has been defined to achieve a hierarchical model about the Fairness characteristics (FCs) or relevant criteria for women with potential influence on each of the goals defined per each use case.  The bottom-up and top down approach converges in a 3 level hierarchized FCs which will allow the interdisciplinary panel to generate Fairness Measures (FMs) for the toolbox and the guidelines.  The AHP (Analytic Hierarchy Process) for qualitative characteristics/criteria and BNs (Bayesian networks) for quantitative characteristics/criteria are the 2 mathematical methodologies used to establish the hierarchy, and a variation of the Rasch model (Section 4.4) is used for the final fairness assessment of the  results in order to validate that not only the criteria have the fairness concept inside established in the bottom-up approach (gathering valuable information directly from women by Focus groups), but also that the fairness concept has not been lost along the process.  The results of this deliverable are a conceptual framework and a methodology, including the impact assessment and the validation of the toolbox and the Decision Support System (DSS), where all the project development will be obligated to be framed in.</description>
    <dc:date>2019-06-27T00:00:00Z</dc:date>
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