Tracking the policy literacy journey of students in a postgraduate diploma course in disability and rehabilitation studies

Health and/or rehabilitation practitioners have to interact with policy decisions. Ideally, they need to be able to understand policies and to engage with them, however, practitioners are often not aware of policies and of how to engage with them. As a post graduate unit with a mandate to develop programmes that respond to practice needs, this article reports on the development of a policy analysis module as part of the Post Graduate Diploma in Disability and Rehabilitation Studies. In this article we report on the development of the module, the approach taken, and on student responses to the module. The course journey of enrolled students is narrated, highlighting the encouragement of student engagement and peer feedback as key to improved learning and understandings in higher education. Facilitators’ use of didactic approaches that centre students and participatory learning seem equally important for meaningful learning

Nonlinear Mechanical Response of DNA due to Anisotropic Bending Elasticity

The response of a short DNA segment to bending is studied, taking into account the anisotropy in the bending rigidities caused by the double-helical structure. It is shown that the anisotropy introduces an effective nonlinear twist-bend coupling that can lead to the formation of kinks and modulations in the curvature and/or in the twist, depending on the values of the elastic constants and the imposed deflection angle. The typical wavelength for the modulations, or the distance between the neighboring kinks is found to be set by half of the DNA pitch.Comment: 4 pages, 3 encapsulated EPS figure

Delivering alcohol IBA: broadening the base from health to non-health contexts: review of the literature and scoping

A review of the literature and scoping on alcohol brief interventions. The review considers the evidence base on the delivery of identification and brief advice in a wide range of settings. It concludes that broader delivery of IBA is feasible, but requires strong organisational support, effective training and financial investment

VPLanet: The Virtual Planet Simulator

We describe a software package called VPLanet that simulates fundamental aspects of planetary system evolution over Gyr timescales, with a focus on investigating habitable worlds. In this initial release, eleven physics modules are included that model internal, atmospheric, rotational, orbital, stellar, and galactic processes. Many of these modules can be coupled simultaneously to simulate the evolution of terrestrial planets, gaseous planets, and stars. The code is validated by reproducing a selection of observations and past results. VPLanet is written in C and designed so that the user can choose the physics modules to apply to an individual object at runtime without recompiling, i.e., a single executable can simulate the diverse phenomena that are relevant to a wide range of planetary and stellar systems. This feature is enabled by matrices and vectors of function pointers that are dynamically allocated and populated based on user input. The speed and modularity of VPLanet enables large parameter sweeps and the versatility to add/remove physical phenomena to assess their importance. VPLanet is publicly available from a repository that contains extensive documentation, numerous examples, Python scripts for plotting and data management, and infrastructure for community input and future development.Comment: 75 pages, 34 figures, 10 tables, accepted to the Proceedings of the Astronomical Society of the Pacific. Source code, documentation, and examples available at https://github.com/VirtualPlanetaryLaboratory/vplane

Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning

Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content. Conventionally, annotator disagreements are resolved before any learning takes place. However, researchers are increasingly identifying annotator disagreement as pervasive and meaningful. They also question the performance of a system when annotators disagree. Particularly when minority views are disregarded, especially among groups that may already be underrepresented in the annotator population. In this paper, we introduce \emph{CrowdOpinion}\footnote{Accepted for publication at ACL 2023}, an unsupervised learning based approach that uses language features and label distributions to pool similar items into larger samples of label distributions. We experiment with four generative and one density-based clustering method, applied to five linear combinations of label distributions and features. We use five publicly available benchmark datasets (with varying levels of annotator disagreements) from social media (Twitter, Gab, and Reddit). We also experiment in the wild using a dataset from Facebook, where annotations come from the platform itself by users reacting to posts. We evaluate \emph{CrowdOpinion} as a label distribution prediction task using KL-divergence and a single-label problem using accuracy measures.Comment: Accepted for Publication at ACL 202