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              <text>A paper investigating the determinants of Olympics success (and lack of), we use a mixed-methods approach, combining qualitative and quantitative analysis to examine the determining factors of sporting success. Using Reiche’s WISE framework, our research extends the analysis of sports performance into the spectre of institutionalist political economy with sports performance as a path-dependent outcome of formal and informal institutions, ideology, early learning, and gender dynamics. Analysing medal outcomes from 1996 to 2016 Olympic Summer Games, alongside building up on new and contemporary research from the wider sports policy subdiscipline, we identify key overperformers such as Australia, China, Cuba, and Jamaica, and underperformers such as India, Nigeria, and Iran.&#13;
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              <text>The project makes an argument that the conflict between Dumitrescu’s and Van Der Akker’s interpretations of metamodernism is only apparent. Instead, I will argue that oscillations or affective captures can occur only in an integrated, continuous milieu, grounded in differences. Deleuze's philosophy of immanence would serve as an important conceptual bridge in reconciling the two frameworks. Hence, metastable positions and narratives would be understood through a process of myth-making. This myth, rather than being fictional, refers to the expression of virtual or minoritarian intensities at the edge of a stratum. They are organised into ‘counter assemblages’ through a process which I called ‘ontostition’, in which the counter assemblages create an immanent positive feedback loop. In this loop, the greater the organisation of an assemblage, the greater the ability to affect and deterritorialise the dominant stratum, releasing the dominant latent tensions and providing the condition for reconstructions. Provide metamodernism with the conceptual framework to understand oscillations in an immanent way, increasing its applications and consistency.</text>
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              <text>With the rise and growing influence of what has been called ‘populist’ leaders and ideas, the concept of populism has gained significant relevance in recent years, both in academic and public discourse. Alongside this trend, anti-populism has emerged as a strong counterforce, critiquing the rise of populism and populist ideas. Scholars have shown that anti-populism, empowered through dominant public and media discourses on populism, has been a powerful lens through which to define the concept of populism itself. The latter has become increasingly misunderstood as a danger to democracy, while there in fact exist a variety of populisms, left- and right-wing, democratic and anti-democratic. This research project will therefore explore the difficulties associated with defining both populism and anti-populism as concepts and investigate the democratic potential of left-wing populist movements that have developed in response to democratic crises. Finally, I will explore how the media engages with populist ideas, figures and parties. This will include a focus on how the written media practises agenda-setting and deflection when discussing populism, as well as the role of populist hype in the euphemisation, trivialisation and amplification of populism through The Guardian’s articles.</text>
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              <text>For portfolio managers seeking implementable portfolio optimization approaches, it is essential to evaluate the models and select the one that delivers the highest after-cost and risk-adjusted performance. This study compares traditional portfolio optimization models (Mean-Variance Optimization and the Black-Litterman model) with machine learning approaches (Random Forest and XGBoost) using the same dataset. The 30 S&amp;P 500 stocks are randomly selected, and their price data from 2015 to 2024, obtained at a monthly frequency from Yahoo Finance, are split into a training window (2015-2021), a validating window (2022), and a testing window (2023-2024). All investment strategies are long-only, fully invested, and rebalanced monthly. An equally weighted portfolio consisting of all 30 selected stocks, applying a buy-and-hold strategy, serves as the benchmark model. The model comparison involves evaluating risk-adjusted portfolio performance and predictive accuracy. The empirical test results from the testing period show that machine learning models, particularly Random Forest, can add significant value to portfolios and achieve good predictive accuracy, but they are cost-sensitive and unstable. In contrast, traditional models perform relatively poorly but are stable under different cost settings.</text>
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