Momentous choices and opportunities have opened up in South Africa since a settlement was successfully negotiated and a new political and constitutional dispensation was created. Events such as the constitutional negotiations and the establishment of reconstruction and development initiatives have placed a renewed emphasis on development management, process facilitation and the development of policy. This study provides an overview of the broad field of policy studies and specifically focuses on policy process models. In particular, this study centres on the further development of the generic process model and provides an overview of the application thereof to the operational environment. From this, simulation exercises and case study material have been developed as policy learning methodologies. Institutional arrangements for policy processes and the institutionalisation of policy and related support capacities at intergovernmental and organisational level receive particular attention. The study demonstrates the application of the generic process model by applying the framework to a case study based on the provincial demarcation exercise. This study concludes that policy management, as a cross-cutting, lateral methodology, in conjunction with similar methodologies, such as strategic planning, research methodology and project management, should be regarded as a critical tool, by the academic community and development practitioners alike, for improving the decision-making capacity of government, the private sector and civil society. / D.Litt. et Phil. (Development Administration)

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents.