David Gauntlett's inaugural lecture, 'Participation culture, creativity, and social change', from 12 November 2008: the key themes in ten minutes

David Gauntlett's inaugural lecture from November 2008, in which he points to a shift from a 'sit down and be told' culture to a more creative 'making and doing' culture, which may offer one of the necessary keys to tackling climate change and environmental problems.

ARTD6071 RESPONSIBLE DESIGN 4

Module Profile

More women in informatics research and education

Speaker: Lynda Hardman Organiser: Time: 04/02/2015 12:30-13:30 Location: B32/3077 Abstract The challenges of addressing gender inequalities in science, technology, engineering, mathematics and medicine is widely acknowledged. We currently hold a bronze award and ECS is one of many academic units in the University which has gained Athena Swan Charter status. In this seminar, Professor Lynda Hardman, Chair of the Informatics Europe working group "Women in Informatics Research and Education” will be explaining the causes of issued underlying gender inequality and constructive routes to addressing this important agenda. In undertaking to commit to an action plan which is a prerequisite of gaining charter status, the University or academic department agreed to accept and incorporate the Athena Swan six principles listed below: * To address gender inequalities requires commitment and action from everyone, at all levels of the organisation * To tackle the unequal representation of women in science requires changing cultures and attitudes across the organisation * The absence of diversity at management and policy-making levels has broad implications which the organisation will examine * The high loss rate of women in science is an urgent concern which the organisation will address * The system of short-term contracts has particularly negative consequences for the retention and progression of women in science, which the organisation recognises * There are both personal and structural obstacles to women making the transition from PhD into a sustainable academic career in science, which require the active consideration of the organisation. This seminar is designed to provide an opportunity to explore these issues NOTE: Lynda will be basing here talk on some of the work she directed as chair of the "Women in Informatics Research and Education” working group. The purpose of the working group is to actively participate and promote actions that contribute to improve gender balance in Information and Communication Sciences and Technologies. The first concrete result of the working group's activities was the publication of the booklet "More Women in Informatics Research and Education" in 2013. The booklet is a compact source of clear and simple best practices to deans and heads of departments that aim to increase the participation of women as both students and employees in their institutions. Many tips included were also inspired by colleagues already in leading positions who have already implemented actions in their institutions to attract more women and ensure their continued participation in the organization at commensurate ratios with their male colleagues. The booklet is endorsed by the European Commission and features a foreword by Neelie Kroes, Vice-President of the European Commission, responsible for the Digital Agenda.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.