Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect : a test of multiple models /

Although alcohol problems and alcohol consumption are related, consumption does not fully account for differences in vulnerability to alcohol problems. Therefore, other factors should account for these differences. Based on previous research, it was hypothesized that risky drinking behaviours, illicit and prescription drug use, affect and sex differences would account for differences in vulnerability to alcohol problems while statistically controlling for overall alcohol consumption. Four models were developed that were intended to test the predictive ability of these factors, three of which tested the predictor sets separately and a fourth which tested them in a combined model. In addition, two distinct criterion variables were regressed on the predictors. One was a measure of the frequency that participants experienced negative consequences that they attributed to their drinking and the other was a measure of the extent to which participants perceived themselves to be problem drinkers. Each of the models was tested on four samples from different populations, including fIrst year university students, university students in their graduating year, a clinical sample of people in treatment for addiction, and a community sample of young adults randomly selected from the general population. Overall, support was found for each of the models and each of the predictors in accounting for differences in vulnerability to alcohol problems. In particular, the frequency with which people become intoxicated, frequency of illicit drug use and high levels of negative affect were strong and consistent predictors of vulnerability to alcohol problems across samples and criterion variables. With the exception of the clinical sample, the combined models predicted vulnerability to negative consequences better than vulnerability to problem drinker status. Among the clinical and community samples the combined model predicted problem drinker status better than in the student samples.

School funding and education policy-making models in Ontario /

The purpose of this thesis is to examine various policy implementation models, and to determine what use they are to a government. In order to insure that governmental proposals are created and exercised in an effective manner, there roust be some guidelines in place which will assist in resolving difficult situations. All governments face the challenge of responding to public demand, by delivering the type of policy responses that will attempt to answer those demands. The problem for those people in positions of policy-making responsibility is to balance the competitive forces that would influence policy. This thesis examines provincial government policy in two unique cases. The first is the revolutionary recommendations brought forth in the Hall -Dennis Report. The second is the question of extending full -funding to the end of high school in the separate school system. These two cases illustrate how divergent and problematic the policy-making duties of any government may be. In order to respond to these political challenges decision-makers must have a clear understanding of what they are attempting to do. They must also have an assortment of policy-making models that will insure a policy response effectively deals with the issue under examination. A government must make every effort to insure that all policymaking methods are considered, and that the data gathered is inserted into the most appropriate model. Currently, there is considerable debate over the benefits of the progressive individualistic education approach as proposed by the Hall -Dennis Committee. This debate is usually intensified during periods of economic uncertainty. Periodically, the province will also experience brief yet equally intense debate on the question of separate school funding. At one level, this debate centres around the efficiency of maintaining two parallel education systems, but the debate frequently has undertones of the religious animosity common in Ontario's history. As a result of the two policy cases under study we may ask ourselves these questions: a) did the policies in question improve the general quality of life in the province? and b) did the policies unite the province? In the cases of educational instruction and finance the debate is ongoing and unsettling. Currently, there is a widespread belief that provincial students at the elementary and secondary levels of education are not being educated adequately to meet the challenges of the twenty-first century. The perceived culprit is individual education which sees students progressing through the system at their own pace and not meeting adequate education standards. The question of the finance of Catholic education occasionally rears its head in a painful fashion within the province. Some public school supporters tend to take extension as a personal religious defeat, rather than an opportunity to demonstrate that educational diversity can be accommodated within Canada's most populated province. This thesis is an attempt to analyze how successful provincial policy-implementation models were in answering public demand. A majority of the public did not demand additional separate school funding, yet it was put into place. The same majority did insist on an examination of educational methods, and the government did put changes in place. It will also demonstrate how policy if wisely created may spread additional benefits to the public at large. Catholic students currently enjoy a much improved financial contribution from the province, yet these additional funds were taken from somewhere. The public system had it funds reduced with what would appear to be minimal impact. This impact indicates that government policy is still sensitive to the strongly held convictions of those people in opposition to a given policy.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.