Equal opportunity offices have been described as agencies of organisational change, and the term ‘cultural change’ has been used to describe aspects of equal opportunity work.Universities are sites of organisational cultures where equal opportunity officers have worked in Australia for the past decade. In this time there have been significant changes to higher education, in terms of the size of the university sector in Australia, and also in terms of funding, governance and management. These changes in universities provided the context for questions about the roles of equal opportunity officers in universities, and how they may have changed. This research investigated the practice of equal opportunity officers in universities by using a grounded theory approach to generate understandings about how this group of university staff may have impacted on university cultures. The study develops links between theories of culture and organisational change that was situated in the practices of equal opportunity officers.The research identified a central conceptual category that was described as ‘picking the pitch’, as the main theme in the work of equal opportunity officers in identifying issues and gaining support for a cultural change agenda. All of the preliminary themes that were identified, and the interactions, the observations and the analysis of culture were prerequisites for ‘picking the pitch’.The thesis uses the research for further reflection and integration of the goals of equal opportunity, and the means that were available to equal opportunity officers to achieve these goals. The interactions of power and influence, and some of the limitations on equal opportunity officers are discussed.In the thesis the usefulness of the concept of culture to equal opportunity officers is examined, along with the underpinning theories about the mind of humanity that contributed to their approach to their task.Finally, the thesis discusses the impact of the research for equal opportunity officers, and for the universities in which they worked.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 170-183).

Thesis--University of Wisconsin. / Mimeograph. eContent provider-neutral record in process. Description based on print version record.

Thesis--University of Wisconsin-Madison. / Includes bibliographical references (leaves 246-251).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 54-60). Also available in electronic version.

Texte remanié de: Th. Ph. D.--Albany--State university of New York. / Bibliogr. p. [261]-279. Index.

Texte remanié de: Proefschrift--Erasmus Universiteit Rotterdam, 1994. / Résumé en anglais. Bibliogr. p. 223-238.

Th. doct--Human geography--University of Stockholm, 1995. / Bibliogr. p. 149-155.

In this dissertation, I outline a theoretical justification for a new world systems analysis in order to understand economic development and underdevelopment, and stratification systems that emerge within nation states because of their global social location. I present my detailed case for amending Wallerstein's World-Systems Analysis by empirically incorporating the interplay of the military, economy and state as opposed to his primarily economic division of labor that defines the core, periphery and semi periphery. I do this by uncovering the latent structure of militarization and its articulation within the world system controlling for state strength. I also outline the basic profile of my Militarized International System (MIS) model based on an extension of C. Wright Mills' Power Elite (1956) thesis and empirically develop the model using a militarized division of labor. With data on 173 nation states, I validate my model through analysis of variance (ANOVA) and multivariate OLS regression. I also outline a theoretical articulation of class, race and gender stratification in the world system informed by the empirical findings. In the end, I make suggestions for "undoing" stratification to inform movements seeking social justice based upon the world-systemic nature of global stratification, where stratification in its articulation cannot be localized and therefore cannot be "fixed" locally within particular nation states.

Les méthodes de Monte Carlo sont des méthodes probabilistes qui utilisent des ordinateurs pour résoudre de nombreux problèmes de la science à l’aide de nombres aléatoires. Leur principal inconvénient est leur convergence lente. La mise au point de techniques permettant d’accélérer la convergence est un domaine de recherche très actif. C’est l’objectif principal des méthodes déterministes quasi-Monte Carlo qui remplacent les points pseudo-aléatoires de simulation par des points quasi-aléatoires ayant une excellente répartition uniforme. Ces méthodes ne fournissent pas d’intervalles de confiance permettant d’estimer l’erreur. Nous étudions dans ce travail des méthodes stochastiques qui permettent de réduire la variance des estimateurs Monte Carlo : ces techniques de stratification le font en divisant le domaine d’échantillonnageen sous-domaines. Nous examinons l’intérêt de ces méthodes pour l’approximation des chaînes de Markov, la simulation de la diffusion physique et la résolution numérique de la fragmentation.Dans un premier chapitre, nous présentons les méthodes de Monte Carlo pour l’intégration numérique. Nous donnons le cadre général des méthodes de stratification. Nous insistons sur deux techniques : la stratification simple (MCS) et la stratification Sudoku (SS), qui place les points sur des grilles analogues à celle du jeu. Nous pressentons également les méthodesquasi-Monte Carlo qui partagent avec les méthodes de stratification certaines propriétés d'équipartition des points d’échantillonnage.Le second chapitre décrit l’utilisation des méthodes de Monte Carlo stratifiées pour la simulation des chaînes de Markov. Nous considérons des chaînes homogènes uni-dimensionnelles à espace d’états discret ou continu. Dans le premier cas, nous démontrons une réduction de variance par rapport `a la méthode de Monte Carlo classique ; la variance des schémas MCSou SS est d’ordre 3/2, alors que celle du schéma MC est de 1. Les résultats d’expériences numériques, pour des espaces d’états discrets ou continus, uni- ou multi-dimensionnels montrent une réduction de variance liée à la stratification, dont nous estimons l’ordre.Dans le troisième chapitre, nous examinons l’intérêt de la méthode de stratification Sudoku pour la simulation de la diffusion physique. Nous employons une technique de marche aléatoire et nous examinons successivement la résolution d’une équation de la chaleur, d’une équation de convection-diffusion, de problèmes de réaction-diffusion (équations de Kolmogorov et équation de Nagumo) ; enfin nous résolvons numériquement l’équation de Burgers. Dans chacun de ces cas, des tests numériques mettent en évidence une réduction de la variance due à l’emploi de la méthode de stratification Sudoku.Le quatrième chapitre décrit un schéma de Monte Carlo stratifie permettant de simuler un phénomène de fragmentation. La comparaison des performances dans plusieurs cas permet de constater que la technique de stratification Sudoku réduit la variance d’une estimation Monte Carlo. Nous testons enfin un algorithme de résolution d’un problème inverse, permettant d’approcher le noyau de fragmentation, à partir de résultats de l’évolution d’une distribution ;nous utilisons dans ce cas des points quasi-Monte Carlo pour résoudre le problème direct. / Monte Carlo methods are probabilistic schemes that use computers for solving various scientific problems with random numbers. The main disadvantage to this approach is the slow convergence. Many scientists are working hard to find techniques that may accelerate Monte Carlo simulations. This is the aim of some deterministic methods called quasi-Monte Carlo, where random points are replaced with special sets of points with enhanced uniform distribution. These methods do not provide confidence intervals that permit to estimate the errordone. In the present work, we are interested with random methods that reduce the variance of a Monte Carlo estimator : the stratification techniques consist of splitting the sampling area into strata where random samples are chosen. We focus here on applications of stratified methods for approximating Markov chains, simulating diffusion in materials, or solving fragmentationequations.In the first chapter, we present Monte Carlo methods in the framework of numerical quadrature, and we introduce the stratification strategies. We focus on two techniques : the simple stratification (MCS) and the Sudoku stratification (SS), where the points repartitions are similar to Sudoku grids. We also present quasi-Monte Carlo methods, where quasi-random pointsshare common features with stratified points.The second chapter describes the use of stratified algorithms for the simulation of Markov chains. We consider time-homogeneous Markov chains with one-dimensional discrete or continuous state space. We establish theoretical bounds for the variance of some estimator, in the case of a discrete state space, that indicate a variance reduction with respect to usual MonteCarlo. The variance of MCS and SS methods is of order 3/2, instead of 1 for usual MC. The results of numerical experiments, for one-dimensional or multi-dimensional, discrete or continuous state spaces show improved variances ; the order is estimated using linear regression.In the third chapter, we investigate the interest of stratified Monte Carlo methods for simulating diffusion in various non-stationary physical processes. This is done by discretizing time and performing a random walk at every time-step. We propose algorithms for pure diffusion, for convection-diffusion, and reaction-diffusion (Kolmogorov equation or Nagumo equation) ; we finally solve Burgers equation. In each case, the results of numerical tests show an improvement of the variance due to the use of stratified Sudoku sampling.The fourth chapter describes a stratified Monte Carlo scheme for simulating fragmentation phenomena. Through several numerical comparisons, we can see that the stratified Sudoku sampling reduces the variance of Monte Carlo estimates. We finally test a method for solving an inverse problem : knowing the evolution of the mass distribution, it aims to find a fragmentation kernel. In this case quasi-random points are used for solving the direct problem.