The French fifth Republic as an opportunity structure:a neo-institutional and cultural approach to the study of leadership politics

The Gaullist settlement of 1958 reconfigured the political institutions of France, introducing into the republican mainstream a new form of leadership politics. Adapting the literature on political opportunity structure (POS) theory, and using the French left as a case study, can help us understand how political parties, ideology and leadership adapt to political institutions and norms. It also illuminates what the consequences are of such adaptation in the contemporary period, particularly as regards the institutionally bound roles of political 'character', protocol and discourse. The paper appraises the relevance and appropriateness of POS theory to leadership politics in France.

Dyslexia research at the turn of the 21st century:behavioural, neuroimaging and genetic findings

Behavioural studies have shown that dyslexics are a heterogeneous population and between-group comparisons are thus inadequate. Some subjects do not develop dyslexia despite having a deficit implicated in this disorder, which points to protective factors. Dyslexia co-occurs with ADHD, DCD, SLI, and SSD, so that future behavioural studies will need to screen and/or statistically control for other disorders. Studies of multiple cases of DPs with other developmental disorders are necessary. Neuroimaging findings show structural and/or functional brain abnormalities in language areas, V5/MT and the cerebellum. Future neuroimaging studies need to investigate the whole reading network and multiple cases. Six dyslexia risk genes have been found, mostly involved in neural migration, which may suggest dyslexia is a deficit of neuronal migration. However, it is not clear how these genes can restrict migration to specific brain areas. As a complex and heterogeneous disorder, dyslexia is likely to be associated with several mutated genes. ADHD and SSD are characterised by genetic risk factors which are partially shared with dyslexia, resulting in comorbidity. Future genetic studies need to focus on identifying other risk genes and pleiotropic genes involved in comorbidities, and linking genotypes implicated in dyslexia with brain structure. Any theory of dyslexia needs to take into account a multitude of risk and protective factors across behavioural, neural and genetic domains.

GTM: the generative topographic mapping

This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis.

Understanding the labour–environmental relationship in Britain, 1967–2011:a new narrative using political opportunity structure and coalition theory

Orthodox depictions of a fraught labour–environmental relationship privileging class, ideological and programmatic differences are problematised by newly quantified evidence of British unions' pro-environmental policy-making since 1967. The following narrative blends widely accepted accounts of the fortunes of both movements with an evaluation of Britain's shifting political opportunity structure and coalition theory to identify an alternative range of constraints and opportunities influencing the propensity and capacity of both movements to interact effectively, culminating recently in unions' emergence as environmental actors in their own right.

What is English for? Language structure and the curriculum for English

This article uses Bernstein’s theory of pedagogic discourse to account for both the processes by which curriculum change occurs and the failure of efforts to introduce meaningful attention to language structure into national curricula.

Developments of the generative topographic mapping

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

An application of strategic group theory to the UK pharmaceuticals industry

Conducts a strategic group mapping exercise by analysing R&D investment, sales/marketing cost and leadership information pertaining to the pharmaceuticals industry. Explains that strategic group mapping assists companies in identifying their principal competitors, and hence supports strategic decision-making, and shows that, in the pharmaceutical industry, R&D spending, the cost of sales and marketing, i.e. detailing, and technological leadership are mobility barriers to companies moving between sectors. Illustrates, in bubble-chart format, strategic groups in the pharmaceutical industry, plotting detailing-costs against the scale of activity in therapeutic areas. Places companies into 12 groups, and profiles the strategy and market-position similarities of the companies in each group. Concludes with three questions for companies to ask when evaluating their own, and their competitors, strategies and returns, and suggests that strategy mapping can be carried out in other industries, provided mobility barriers are identified.

Strategic group theory:review, examination and application in the UK pharmaceutical industry

Purpose – The purpose of this paper is to consider the current status of strategic group theory in the light of developments over the last three decades. and then to discuss the continuing value of the concept, both to strategic management research and practising managers. Design/methodology/approach – Critical review of the idea of strategic groups together with a practical strategic mapping illustration. Findings – Strategic group theory still provides a useful approach for management research, which allows a detailed appraisal and comparison of company strategies within an industry. Research limitations/ implications – Strategic group research would undoubtedly benefit from more directly comparable, industry-specific studies, with a more careful focus on variable selection and the statistical methods used for validation. Future studies should aim to build sets of industry specific variables that describe strategic choice within that industry. The statistical methods used to identify strategic groupings need to be robust to ensure that strategic groups are not solely an artefact of method. Practical implications – The paper looks specifically at an application of strategic group theory in the UK pharmaceutical industry. The practical benefits of strategic groups as a classification system and of strategic mapping as a strategy development and analysis tool are discussed. Originality/value – The review of strategic group theory alongside alternative taxonomies and application of the concept to the UK pharmaceutical industry.