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The Anthropocene presents novel challenges for environmental management. This paper considers the challenges that the Anthropocene poses for social learning techniques in adaptive management. It situates these challenges with respect to how anthropogenic forcing on the Earth system affects the conditions required for: (1) The cooperative exercises of social learning; (2) The techniques used for assessing the fit of institutions to social-ecological systems; and, (3) The strategies employed for identifying management targets that are transformed by human action. In view of these challenges, the paper then examines how the practices of shadow networks may provide paths for incorporating a broader, more robust suite of social learning practices in the Anthropocene. The paper emphasizes how novel challenges in the Anthropocene demand increased attention to ethical practices, particularly those that establish center-periphery relationships between social learning communities and shadow networks. [ABSTRACT FROM AUTHOR]

The emerging discipline of Animal–Computer Interaction (ACI) aims to take what in Interaction Design is known as a user-centred approach to the design of technology intended for animals, placing them at the centre of the design process as stakeholders, users, and contributors. However, current regulatory frameworks for the involvement of animals in research are not animal-centred, regarding them as research instruments, unable to consent to procedures that may harm them, rather than consenting research participants and design contributors. Such frameworks aim to minimise the impacts of research procedures on the welfare of individual animals, but this minimisation is subordinated to specific scientific and societal interests, and to the integrity of the procedures required to serve those interests. From this standpoint, the universally advocated principles of replacement, reduction and refinement aim to address the ethical conflicts arising from the assumed inability of individual animals to consent to potentially harmful procedures, but such principles in fact reflect a lack of individual centrality. This paper makes the case for moving beyond existing regulations and guidelines towards an animal-centred framework that can better support the development of ACI as a discipline. Firstly, recognising animal welfare as a fundamental requirement for users and research participants alike, the paper articulates the implications of a welfare-centred ethics framework . Secondly, recognising consent as an essential requirement of participation, the paper also defines criteria for obtaining animals׳ mediated and contingent consent to engaging with research procedures. Further, the paper argues for the methodological necessity, as well as the ethical desirability, of such an animal-centred framework, examining the boundaries of its applicability as well as the benefits of its application. Finally, the paper puts forward a series of practical principles for conducting ACI research, which imply but also essentially exceed the welfare and ethics requirements of current regulatory frameworks. [ABSTRACT FROM AUTHOR]

• The current paper provides an overview of human evaluation practices in NLG. • The current paper gives an overview of the steps necessary to undertake a human evaluation study. • Building on findings from NLG, but also statistics and the behavioral sciences, the current paper provides a set of recommendations and best practices for human evaluation in NLG. Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated, with a particularly high degree of variation in the way that human evaluation is carried out. This paper provides an overview of how (mostly intrinsic) human evaluation is currently conducted and presents a set of best practices, grounded in the literature. These best practices are also linked to the stages that researchers go through when conducting an evaluation research (planning stage; execution and release stage), and the specific steps in these stages. With this paper, we hope to contribute to the quality and consistency of human evaluations in NLG. [ABSTRACT FROM AUTHOR]