What is project governance? Disclosing the source of confusion and revealing the essence of governance

The governance of project work is well discussed in the extant literature that explores the relationship between projects and their parent organisations. And governance is a well-known term amongst senior management, project practitioners, and stakeholders. However, as this thesis reveals and attempts to address, ‘what is governance’ is actually the subject of much confusion across scholarly literature, practitioner publications and project managers themselves. Identifying and resolving such confusion is fundamental to progressing the discipline because, as proposed by this thesis, governance is the system by which projects are directed and controlled. This thesis by publication: 1. Identifies the definitional confusion surrounding project governance, governance generally and many other associated project management terms. 2. Develops a ‘refined’ definitional method for resolving confusion concerning conceptual definitions. 3. Applies this method to develop refined (internally consistent) definitions of governance and related and associated terms. 4. Reveals the lack of genericity at the core of some project management practitioner documents and methodology. 5. Identifies and resolves 10 different issues that cause definitional confusion in conceptual terms. 6. Provides a philosophical justification for the resolution of each of these issues by critically examining Aristotle’s, Mill’s, Wittgenstein’s, and Popper’s work in relation to definitions. 7. Develops a set of axioms and definitional rules for avoiding conflict resulting from definitional confusion. 8. Proposes a theory of meaning for conceptual terms in management

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal