Partnership, politics and public administration: a case study of the Ontario Ministry of Natural Resources

Efforts to reform the public sector reflect the social, political and economic environment within which government must function. The recent demands by the public for more consensual decision-making, as well as more efficient, effective and responsive public service, have resulted in a number of reform initiatives, including an emphasis on partnership development. The purpose of this thesis is to examine partnership arrangements within the public sector. Specifically, the thesis will assess the value of partnerships and their impact on government by examining six partnership arrangements involving the Ontario Ministry of Natural Resources (OMNR). The OMNR, having recently been awarded the 1992 Institute of Public Administration of Canada Award for Innovative Management, on the theme of partnership development, is being lauded as an example for other government agencies considering similar alliances. The thesis begins by introducing the concept and practice of partnership within the public sector in general and the OMNR specifically. Descriptive analysis of six OMNR partnerships is provided and a number of criteria are used to determine the success of each of these arrangements. Special attention is paid to the political implications of partnerships and to those attributes which appear to contribute to the successful establishment and iii maintenance of partnership arrangements. The conclusion is drawn that partnerships provide the government with an opportunity to address public demands for greater involvement in decision-making while accommodating government's limited financial resources. However, few truly collaborative partnerships exist within the public sector. There are also significant political implications associated with partnerships which must be dealt with both at the political and bureaucratic levels of government. Lastly, it is argued that while partnerships within the OMNR are experiencing some difficulties, they constitute a genuine attempt to broaden the base of decision-making and to incorporate the concerns of stakeholders into resource management.

Automatic evolution of conceptual building architectures

This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.

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.

The influence of cognitive resources on compensatory arm responses

The purpose of this study was to determine the influence of an ongoing cognitive task on an individual’s ability to generate a compensatory arm response. Twenty young and 16 older adults recovered their balance from a support surface translation while completing a cognitive (counting) task of varying difficulty. Surface electromyographic (EMG) recordings from the shoulders and kinematics of the right arm were collected to quantify the compensatory arm response. Results indicated that the counting task, regardless of its difficulty as well as the age of the individual, had minimal influence on the onset or magnitude of arm muscle activity that occurred following a loss of balance. In contrast to previous research, this study’s findings suggest that the cortical or cognitive resources utilized by the cognitive task are not relied upon for the generation of compensatory arm responses and that older adults are not disproportionately affected by dual-tasking than young adults.

The time-course of lexical activation in Japanese morphographic word recognition: Evidence for a character-driven processing model

This lexical decision study with eye tracking of Japanese two-kanji-character words investigated the order in which a whole two-character word and its morphographic constituents are activated in the course of lexical access, the relative contributions of the left and the right characters in lexical decision, the depth to which semantic radicals are processed, and how nonlinguistic factors affect lexical processes. Mixed-effects regression analyses of response times and subgaze durations (i.e., first-pass fixation time spent on each of the two characters) revealed joint contributions of morphographic units at all levels of the linguistic structure with the magnitude and the direction of the lexical effects modulated by readers’ locus of attention in a left-to-right preferred processing path. During the early time frame, character effects were larger in magnitude and more robust than radical and whole-word effects, regardless of the font size and the type of nonwords. Extending previous radical-based and character-based models, we propose a task/decision-sensitive character-driven processing model with a level-skipping assumption: Connections from the feature level bypass the lower radical level and link up directly to the higher character level.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Explaining voice behaviour: The roles of personal resources, social interdependence, and supervisor leadership style

This study contributes to current research on voice behaviour by investigating several under-explored drivers that motivate employees’ expression of constructive ideas about work-related issues. It draws from the concept of psychological climate to examine how voice behaviour is influenced by employees’ (1) personal resources (tenacity and passion for work), (2) perceptions of social interdependence (task and outcome interdependence), and (3) supervisor leadership style (transformational and transactional). Using a multi-source research design, surveys were administered to 226 employees and to 24 supervisors at a Canadian-based not-for-profit organization. The hypotheses are tested with hierarchical regression analysis. The results indicate that employees are more likely to engage in voice behaviour to the extent that they exhibit higher levels of passion for work. Further, their voice behaviour is lower to the extent that their supervisor adopts a transformational leadership style characterized by high performance expectations or a transactional leadership style based on contingent rewards and contingent punishment behaviours. The study reveals that there are no significant effects of tenacity, social interdependence, and behaviour-focused transformational leadership on voice. The findings have significant implications for organizations that seek to encourage employee behaviours that help improve current work practices or undo harmful situations.