How Social Media Propels Social Movements

Social movements and collective action have been a crucial point of inquiry for academic fields such a sociology, psychology, and social justice throughout the last half century. Although, a relatively young phenomenon in our world, the influence of social media on social development, particularly the use of social media as a communicative and organizational tool to promote progressive social change, requires serious consideration (Bogen, Bleiweiss & Orchowski, 2019). The #MeToo movement is a recent and ongoing example of a social movement reframing global conversations around sexual violence, which has been propelled by the use of social media platforms (Bogen, Bleiweiss & Orchowski, 2019). This has had impact on local and global levels, as social media gives a platform for more voices to be heard on issues impacting society. This has expanded the concept and expectations of activism through the formation of communities in the digital world with a collective identity (Zarkov & Davis, 2018). In the following sections of this paper, I will define social media and social movements as separate entities, explore how social media has reinforced social movements such as #MeToo, the importance of established community within social movements, and consider how a strong sense of belonging can promote equitable outcomes. While social media is not the only variable of significance for contemporary social movements, when used effectively, it has the potential to cross cultural and national barriers, challenge social norms and promote equity for all people. This will be explored in an attempt to expand societal awareness around the potential benefits and ramifications of social media\u27s influence on social movements

Metalliferous Biosignatures for Deep Subsurface Microbial Activity

Acknowledgments We thank the British Geological Survey (BGS) for the provision of samples and the Science & Technology Facilities Council (STFC) grant (ST/L001233/1) for PhD funding which aided this project. Research on selenium in reduction spheroids was also supported by NERC grants (NE/L001764/1 and NE/ M010953/1). The University of Aberdeen Raman facility was funded by the BBSRC. We also thank John Still for invaluable technical assistance.Peer reviewedPublisher PD

Inferring ocean transport statistics with probabilistic neural networks

Using a probabilistic neural network and Lagrangian observations from the Global Drifter Program, we model the single particle transition probability density function (pdf) of ocean surface drifters. The transition pdf is represented by a Gaussian mixture whose parameters (weights, means and covariances) are continuous functions of latitude and longitude determined to maximise the likelihood of observed drifter trajectories. This provides a comprehensive description of drifter dynamics allowing for the simulation of drifter trajectories and the estimation of a wealth of dynamical statistics without the need to revisit the raw data. As examples, we compute global estimates of mean displacements over four days and lateral diffusivity. We use a probabilistic scoring rule to compare our model to commonly used transition matrix models. Our model outperforms others globally and in three specific regions. A drifter release experiment simulated using our model shows the emergence of concentrated clusters in the subtropical gyres, in agreement with previous studies on the formation of garbage patches. An advantage of the neural network model is that it provides a continuous-in-space representation and avoids the need to discretise space, overcoming the challenges of dealing with nonuniform data. Our approach, which embraces data-driven probabilistic modelling, is applicable to many other problems in fluid dynamics and oceanography

Increased biomass and carbon burial 2 billion years ago triggered mountain building

The work was partially supported by NERC grant NE/M010953/1. The manuscript benefitted by advice from Michael Brown and Ross Mitchell.Peer reviewedPublisher PD