University and College Union

Unions and collective negotiation are essential in maintaining good working conditions for all staff, in particular those who are on Fixed Term Contracts (FTC) and are often starting out in their academic/research careers. The FTC group is particularly vulnerable to discrimination and the pressure to produce outputs and bring in funding to secure more secure employment. The very nature of being on a FTC greatly reduces the amount of funding sources that can be applied to.This talk provides an overview of the University and College Union (UCU), how it operates, what the benefits are, what we have achieved nationally and at a local level. Joe Viana, a FTC research er at the University, and the FTC rep of the Southampton UCU branch, will be on hand to answer questions and to provide feedback on local and national level activities.The talk should be of interest to all FTC staff, their supervisors and any postgrads considering a research career in higher education.

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.