Explaining party cohesion and discipline in democratic legislatures: purposiveness and contexts

Legislative party discipline and cohesion are important phenomena in the study of political systems. Unless assumptions are made that parties are cohesive and act as unified collectivities with reasonably well-defined goals, it is really difficult, if not impossible, to consider their electoral and legislative roles usefully. But levels of legislative party cohesiveness are also important because they provide us with crucial information about how legislatures/ parliaments function and how they interact with executives/governments. Without cohesive (or disciplined) parties,1 government survival in parliamentary systems is threatened because executive and legislative powers are fused while in separated systems presidents' bases of legislative support become less stable. How do we explain varying levels of legislative party cohesion? The first part of this article draws on the purposive literature to explore the benefits and costs to legislators in democratic legislatures of joining and acting collectively and individualistically within political parties. This leads on to a discussion of various conceptual and empirical problems encountered in analysing intra-party cohesion and discipline in democratic legislatures on plenary votes. Finally, the article reviews the extant empirical evidence on how a multiplicity of systemic, party-levels and situational factors supposedly impact cohesion/discipline levels. The article ends with a discussion of the possibilities and limitations of building comparative models of cohesion/discipline.

A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps

There is still a lack of effective paradigms and tools for analysing and discovering the contents and relationships of project knowledge contexts in the field of project management. In this paper, a new framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps under big data environments is proposed and developed. The conceptual paradigm, theoretical underpinning, extended topic model, and illustration examples of the ontology model for project knowledge maps are presented, with further research work envisaged.