Public Sector Added Value: Can Bureaucracy Deliver?

This paper takes stock of our understanding of the 'architecture' of public sector resource allocation mechanisms. It is a speculative venture and provides a framework for thinking about issues rather than a completed theoretical model. The concept of architecture is borrowed from the design sciences and is used to explore the conditions of performativity within networks of relational contracts. The age-old question of markets versus hierarchy is too simplistic. Instead, the search is for optimal complex network relationships that are based upon co-operation and participation rather than competition and control. Within these networks the public sector, it is argued, has a new role of acting as a broker in the creation of value. Judging the public sector's relative effectiveness in the creation of value also requires closer attention to be given to the context within which public sector managers take decisions. In particular it is necessary to acknowledge that they confront the 'wicked' problems of society that the electorate demand to be solved. This gives renewed interest in the notion of market failure.     

Simultaneous identification and δ¹³C classification of drugs using GC with concurrent single quadrupole and isotope ratio mass spectrometers

In this study, δ13C values of six cocaine samples were identified and classified using a single quadrupole mass spectrometer and an isotope ratio mass spectrometry (IRMS) as simultaneous gas chromatography detectors. Our instrument modification is simple to use and is useful (i) when the sample is of limited size or can only be injected once, (ii) to help identify peaks in a complicated IRMS chromatogram, and (iii) to help differentiate very simple systems when impurity profiling is not possible. The EI-MS confirmed the identity of cocaine in each sample. The IRMS data distinguished 12 of the 15 possible pair-wise comparisons at the 95% CL. Three samples could not be differentiated by their δ13C ratios for cocaine. ANOVA demonstrated that the measurement variance was consistently larger than the sample variance. As the δ13C values clearly show, this technique enables the exclusion of a potential common source even when two samples have otherwise identical chemical and physical properties.