Identifying capacity building cluster impact in the West Midlands

specific themes in Energy, Health Technologies and Finance via KTP (6), CASE (18), Placement (6) and Voucher (3) projects across the themes. Through the analysis of responses to an assessment questionnaire, reports from and interviews with a number of researchers, academics and industry sponsors engaged in KTP, CASE, and Placement projects we attempt to identify, analyse and assess the impact of these research projects. We adopt the Research Councils UK (RCUK) definition of research impact as 'the demonstrable contribution that excellent research makes to society and the economy'. In addition to identifying academic impact, we identify evidence of social and economic impact, for example, that it has been taken up and used by policy makers, and practitioners and has led to improvements in services or business. Helpful and un-helpful factors, identified during the execution of the research projects, are also considered

Editorial

"Editors' Introduction." Global Labour Journal (January) 7(1). Rina Agarwala, Jenny Chan, Alexander Gallas, and Ben Scull

Defining and characterizing Aflatoxin contamination risk areas for corn in Georgia, USA: Adjusting for collinearity and spatial correlation

Aflatoxin is a carcinogenic toxin to humans and animals produced by mold fungi in staple crops. Surveys of Aflatoxin are expensive, and the results are usually not available for implementing within season mitigation strategies. Identification of high and low risk areas and years is essential to reduce the number of samples analyzed for Aflatoxin concentration. Previously a risk factors approach was developed to determine county level Aflatoxin contamination risk in southern Georgia, but Aflatoxin concentrations and risk factor data were not analyzed simultaneously and all risk factors had equal weight which is unrealistic. In the current paper we propose a regression approach to overcome these problems. Spatial Poisson profile regression identified clusters of counties which have similar Aflatoxin risk and risk factor profiles, whilst explicitly taking into account multicollinearity in the risk factor data and spatial autocorrelation in the Aflatoxin data. This approach allows examination of the utility of different highly correlated variables including remotely sensed data that could give information at the sub-county level. The results identify plausible clusters compared to previous work but also give the relative importance of the risk factors associated with those clusters. The approach also helps show that some factors like well-drained soil behave differently from expectations and irrigation data is not useful