A crisis of social democracy : organized labour and the NDP in an era of neoliberalism

The NDP was founded out of the ashes of the Co-Operative Commonwealth Federation to cooperate with the Canadian Labour Congress to become the 'political arm of organized labour' in Canada. The NDP has long claimed they are the party which represents the policy goals of organized labour in Canada: that the NDP alone will fight for trade union rights, and will fight for Canadian workers. Divergent Paths is an examination of the links between the labour movement and the ND P in an era ofneo-liberalism. Provincial NDP governments have become increasingly neoliberal in their ideological orientation, and have often proved to be no friend to the labour movement when they hold office. The Federal party has never held power, nor have they ever formed the Official Opposition. This thesis charts the progress of the federal NDP as they become more neoliberal from 1988 to 2006, and shows how this trend effects the links between the NDP and labour. Divergent Paths studies each federal election from 1988 to 2006, looking at the interactions between Labour and the NDP during these elections. Elections provide critical junctions to study discourse - party platforms, speeches, and other official documents can be used to examine discourse. Extensive newspaper searches were used to follow campaign events and policy speeches. Studying the party's discourse can be used to determine the ideological orientation of the party itself: the fact that the party's discourse has become neoliberal is a sure sign that the party itself is neoliberal. The NDP continues to drive towards the centre of the political spectrum in an attempt to gain multi-class support. The NDP seems more interested in gaining seats at any cost, rather then promoting the agenda of Labour. As the party attempts to open up to more multi-class support, Labour becomes increasingly marginalised in the party. A rift which arguably started well before the 1988 election was exacerbated during that election; labour encouraged the NDP to campaign solely on the issue of Free Trade, and the NDP did not. The 1993 election saw the rift between the two grow even further as the Federal NDP suffered major blowbacks from the actions of the Ontario NDP. The 1997 and 2000 elections saw the NDP make a deliberate move to the centre of the political spectrum which increasingly marginalised labour. In the 2004 election, Jack Layton made no attempt to move the party back to the left; and in 2006 the link between labour and the NDP was perhaps irreparably damaged when the CAW endorsed the Liberal party in a strategic voting strategy, and the CLC did not endorse the NDP. The NDP is no longer a reliable ally of organized labour. The Canadian labour movement must decide wether the NDP can be 'salvaged' or if the labour movement should end their alliance with the NDP and engage in a new political project.

Group-invariant solutions of semilinear Schrodinger equations in multi-dimensions

Symmetry group methods are applied to obtain all explicit group-invariant radial solutions to a class of semilinear Schr¨odinger equations in dimensions n = 1. Both focusing and defocusing cases of a power nonlinearity are considered, including the special case of the pseudo-conformal power p = 4/n relevant for critical dynamics. The methods involve, first, reduction of the Schr¨odinger equations to group-invariant semilinear complex 2nd order ordinary differential equations (ODEs) with respect to an optimal set of one-dimensional point symmetry groups, and second, use of inherited symmetries, hidden symmetries, and conditional symmetries to solve each ODE by quadratures. Through Noether’s theorem, all conservation laws arising from these point symmetry groups are listed. Some group-invariant solutions are found to exist for values of n other than just positive integers, and in such cases an alternative two-dimensional form of the Schr¨odinger equations involving an extra modulation term with a parameter m = 2−n = 0 is discussed.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.