Dehumanisation involves the perception and/or treatment of a social group's members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others.

The growth of the "gig economy" in recent years has revolutionised the way that millions of people work. Proponents argue that the gig economy gives people more flexibility and opportunities and lowers barriers of entry to the labour market, while detractors say that it erodes workplace regulations and standards while encouraging businesses to treat workers as increasingly disposable. No matter which side of the debate you fall on, it's clear that the gig economy is here to stay. But with more and more people signing up for these flexible and freelance work arrangements, how can businesses manage them effectively? Enter "algorithmic management": the use of algorithms to oversee the efforts of human workers. As algorithmic management becomes more commonplace, it's important to understand what this practice is, the pros and cons of using it, and what the future holds.

The growth of the "gig economy" in recent years has revolutionised the way that millions of people work. Proponents argue that the gig economy gives people more flexibility and opportunities and lowers barriers of entry to the labour market, while detractors say that it erodes workplace regulations and standards while encouraging businesses to treat workers as increasingly disposable. No matter which side of the debate you fall on, it's clear that the gig economy is here to stay. But with more and more people signing up for these flexible and freelance work arrangements, how can businesses manage them effectively? Enter "algorithmic management": the use of algorithms to oversee the efforts of human workers. As algorithmic management becomes more commonplace, it's important to understand what this practice is, the pros and cons of using it, and what the future holds.

Everyone knows about the Turing Test. It was first proposed by Alan Turing in his famous 1950 paper'On Computing Machinery and Intelligence'. The paper started with the question'Can a machine think?'. Turing noted that philosophers would be inclined to answer that question by hunting for a definition. They would identify the necessary and sufficient conditions for thinking and then they would try to see whether machines met those conditions. They would probably do this by closely investigating the ordinary language uses of the term'thinking' and engaging in a series of rational reflections on those uses. At least, Oxbridge philosophers in the 1950s would have been inclined to do it this way. Turing thought this approach was unsatisfactory.